@conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral23, title = {Brain-age prediction and its associations with glial and synaptic CSF markers}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Karine Fauria and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral22, title = {Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer{\textquoteright}s disease and neurodegeneration stratified by sex}, booktitle = {15th Clinical Trials on Alzheimer{\textquoteright}s Disease Conference (CTAD)}, year = {2022}, month = {11/2022}, address = {San Francisco, USA}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Luigi Lorenzini and Alle Meije Wink and H. Mutsaerts and Sven Haller and Ga{\"e}l Chetelat and Frederik Barkhof and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2022}, month = {07/2022}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Greg Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aCasamitjanab, title = {NeAT: a nonlinear analysis toolbox for neuroimaging}, journal = {Neuroinformatics}, year = {2020}, month = {03/2020}, abstract = {

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer{\textquoteright}s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

}, keywords = {Alzheimer{\textquoteright}s disease, APOE, GAM, GLM, inference, neuroimaging, nonlinear., SVR}, doi = {10.1007/s12021-020-09456-w}, url = {https://link.springer.com/article/10.1007/s12021-020-09456-w}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Santi Puch and Asier Aduriz and Carlos Lopez and G. Operto and R. Cacciaglia and C. Falcon and J.L. Molinuevo and Juan D. Gispert} } @article {aCasamitjanac, title = {Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer{\textquoteright}s disease}, journal = {Frontiers in Neurology, section Applied Neuroimaging}, volume = {11}, year = {2020}, month = {07/2020}, chapter = {648}, abstract = {

Alzheimer{\textquoteright}s disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers such as cerebrospinal fluid (CSF) levels of Aβ, p-tau and t-tau. In parallel, brain anatomy can be characterized through imaging techniques such as magnetic resonance imaging (MRI). In this work, we relate both sets of measurements seeking associations between biomarkers and brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we use the projection to latent structures (PLS)\ method. Using PLS, we find a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We look for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we use a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We apply this technique to the study of subjects in the whole AD continuum from the preclinical asymptomatic stages all through to the symptomatic groups. Subsequent analyses involve splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitive impaired subjects (MCI) and dementia subjects (AD-dementia) where all symptoms are due to AD.

}, keywords = {Alzheimer{\textquoteright}s disease, brain morphology, CSF biomarkers, Latent model, PLS, preclinical AD}, author = {Adri{\`a} Casamitjana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @article {aPetrone, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI}, journal = {Alzheimer{\textquoteright}s Research \& Therapy}, volume = {11}, year = {2019}, month = {12/2019}, abstract = {

Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer{\textquoteright}s disease (AD) pathophysiologic continuum constituting what has been established as {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.

Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cut-offs (\<192pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.

Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt\>2.5 years and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC=0.87 (95\%CI:0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior and lateral temporal lobes, precuneus, caudate heads, basal forebrain and lateral ventricles.

Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid positive individuals, this longitudinal voxelwise classifier is expected to avoid 55\% of unnecessary CSF and/or PET scans and reduce economic cost by 40\%.

}, doi = {https://doi.org/10.1186/s13195-019-0526-8}, url = {https://link.springer.com/article/10.1186/s13195-019-0526-8}, author = {Paula Petrone and Adri{\`a} Casamitjana and Carles Falcon and Miguel Artigues C{\`a}naves and G. Operto and R. Cacciaglia and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @article {aCasamitjana, title = {MRI-Based Screening of Preclinical Alzheimer{\textquoteright}s Disease for Prevention Clinical Trials}, journal = {Journal of Alzheimer{\textquoteright}s Disease}, volume = {64}, year = {2018}, month = {07/2018}, chapter = {1099}, abstract = {

The identification of healthy individuals harboring amyloid pathology constitutes one important challenge for secondary prevention clinical trials in Alzheimer{\textquoteright}s disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60\% unnecessary CSF/PET tests and to reduce 47\% of the cost of recruitment when used in a simulated clinical trial setting. This recruitment strategy capitalizes on already acquired MRIs to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for AD screening. This protocol could foster the development of secondary prevention strategies for AD.

}, author = {Adri{\`a} Casamitjana and Paula Petrone and Alan Tucholka and Carles Falcon and Stavros Skouras and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @conference {cPuch, title = {Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling}, booktitle = {NIPS 2016 Workshop on Machine Learning for Health}, year = {2016}, month = {12/2016}, author = {Santi Puch and Asier Aduriz and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Juan D. Gispert} }