@article {aCumplido-Mayorala, title = {Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers}, journal = {Molecular psychiatry}, year = {2025}, month = {04/2025}, abstract = {

Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer{\textquoteright}s disease (AD) and other neurodegenerative diseases. However, no previous studies have investigated the relationship between specific pathophysiological pathways in AD and biological brain aging. Here, we studied whether glial reactivity and synaptic dysfunction are associated with biological brain aging in the earliest stages of the Alzheimer{\textquoteright}s\ continuum, and if these mechanisms are differently associated with AD-related cortical atrophy. We further evaluated their effects on cognitive decline. We included 380 cognitively unimpaired individuals from the ALFA+ study, for which we computed their brain-age deltas by subtracting chronological age from their brain age predicted by machine learning algorithms. We studied the cross-sectional linear associations between brain-age delta and cerebrospinal fluid (CSF) biomarkers of synaptic dysfunction (neurogranin, GAP43, synaptotagmin-1, SNAP25, and α-synuclein), glial reactivity (sTREM2, YKL-40, GFAP, and S100b) and inflammation (interleukin-6). We also studied the cross-sectional linear associations between AD signature and these CSF biomarkers, We further evaluated the mechanisms linking baseline brain-age delta and longitudinal cognitive decline by performing mediation analyses. To reproduce our findings on an independent cohort, we included 152 cognitively unimpaired and 310 mild cognitive impaired (MCI) individuals from the ADNI study. We found that higher CSF sTREM2 was associated with a younger brain-age after adjusting for AD pathology, both in ALFA+ cognitively unimpaired and in ADNI MCI individuals. Furthermore, we found that CSF sTREM2 fully mediated the link between older brain-age and cognitive decline in ALFA+. In summary, we showed that the protective microglial state reflected by higher CSF sTREM2 has a beneficial impact on biological brain aging that may partly explains the variability in cognitive decline in early AD stages, independently of AD pathology.

}, issn = {1359-4184}, doi = {https://doi.org/10.1038/s41380-025-02961-x}, url = {https://www.nature.com/articles/s41380-025-02961-x}, author = {Irene Cumplido-Mayoral and Gonzalo S{\'a}nchez-Benavides and Marta Mil{\`a} Alom{\`a} and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Jose L. Molinuevo and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, journal = {Nature Computational Science}, year = {2023}, month = {07/2023}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. Indeed, the equivalent of one month of continuous compute can be reduced to hours. In addition, Neural ADMIXTURE can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. Our models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, booktitle = {2021 Society for Molecular Biology \& Evolution meeting (SMBEv2021)}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @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} } @mastersthesis {xPuch16, title = {Nonlinear analysis toolbox for neurodegenerative diseases and aging}, year = {2016}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Neurodegenerative diseases impose substantial public health burdens on populations
throughout the world. Alzheimer{\textquoteright}s disease is among the major neurodegenerative diseases,
and its causes and treatment are still unknown. Researchers around the world are conducting
large data-driven studies in order to unveil the causes and biological mechanisms of such
diseases, and for that reason automatic tools that allow to uncover statistically signi cant
ndings are needed.
To address this problem we present in this thesis a software toolbox that provides the
tools to analyze the linear and nonlinear dynamics of gray-matter and study the statistical
signi cance of such dynamics at the voxel level. The toolbox features various tting methods
and t evaluation metrics, an automatic hyperparameters look-up algorithm and several
visualization and comparison tools.
All the features provided in this toolbox were tested in two real problems provided by
the Pasqual Maragall Foundation, and it yielded results that were validated by the ndings
in the original studies.

}, author = {Santi Puch}, editor = {Ver{\'o}nica Vilaplana} } @inbook {bDigne15, title = {Neighborhood Filters and the Recovery of 3D Information}, booktitle = {Handbook of Mathematical Methods in Imaging}, year = {2015}, pages = {1645-1673}, publisher = {Springer Verlag}, organization = {Springer Verlag}, edition = {2}, isbn = {978-1-4939-0789-2}, author = {J. Digne and Dimiccoli, M. and N. Sabater and Salembier, P.} } @article {aBellot Pujalte15, title = {NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {09/2015}, abstract = {

Background:
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results:
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions:
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

}, keywords = {Benchmark, Bioconductor package, Gene expression, Gene regulation network reconstruction, Gene regulatory networks, Synthetic genetic networks}, doi = {10.1186/s12859-015-0728-4}, url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0728-4}, author = {P. Bellot and Olsen, Catharina and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @conference {cNguyen, title = {NII-HITACHI-UIT at TRECVID 2015 Instance Search}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

In this paper, we propose two methods to improve last year instance search framework. Both of them are based on post processing scheme that try to rerank top K shots returned from BOW model. The rst system is to propose a query-adaptive weighting technique between DPM object detectors score and BOW{\textquoteright}s score. In order to nd a good weight, we use a neural network which learns characteristics of the query including number of features, number of shared words and area of the query topic. The second system combines two state-of-the-art object detectors: DPM and Fast RCNN to estimate object location and similarity score, respectively. The nal score is computed using these components together with BOW based similarity score returned from the baseline system. The experimental results show that our system improved pretty much even with a smaller number of top K input ranked list. Compared to other teams, we got the second place with the same run.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Nguyen, Vinh-Tiep and Duy-Dinh-Le and Amaia Salvador and Caizhi-Zhu and Nguyen, Dinh-Luan and Tran, Minh-Triet and Ngo Duc, Thanh and Anh Duong, Duc and Satoh, Shin{\textquoteright}ichi and Xavier Gir{\'o}-i-Nieto} } @article {aBarkhuus14, title = {New interaction modes for rich panoramic live video experiences}, journal = {Behaviour \& Information Technology}, volume = {33}, year = {2014}, month = {07/2014}, chapter = {859-869}, abstract = {

The possibilities of panoramic video are based on the capabilities of high-resolution digital video streams and higher band- width{\textquoteright}s opportunities to broadcast, stream and transfer large content across platforms. With these opportunities also come challenges such as how to focus on sub-parts of the video stream and interact with the content shown on a large screen. In this paper, we present studies of two different interaction modes with a large-scale panoramic video for live experiences; we focus on interactional challenges and explore if it is (1) possible to develop new interactional methods/ways of approaching this type of high-resolution content and (2) feasible for users to interact with the content in these new ways. We developed prototypes for two different interaction modes: an individual system on a mobile device, either a tablet or a mobile phone, for interacting with the content on the same and a non-touch gesture-based system for the home or small group interaction. We present pilot studies where we explore the possibilities and challenges with these two interaction modes for panoramic content.

}, keywords = {interaction modes, interactive television, panoramic video, pilot studies}, doi = {10.1080/0144929X.2014.914975}, url = {http://www.tandfonline.com/doi/full/10.1080/0144929X.2014.914975}, author = {Barkhuus, Louise and Zoric, Goranka and Engstr{\"o}m, Arvid and Ruiz-Hidalgo, J. and Verzijp, Nico} } @inbook {bDigne11, title = {Neighborhood Filters and the Recovery of 3D Information}, booktitle = {Handbook of Mathematical Methods in Imaging}, year = {2011}, pages = {1203-1229}, publisher = {Springer Verlag}, organization = {Springer Verlag}, isbn = {978-0-387-92919-4}, author = {J. Digne and Dimiccoli, M. and Salembier, P. and N. Sabater} } @conference {cValero10, title = {New hyperspectral data representation using binary partition tree}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010}, year = {2010}, pages = {80{\textendash}83}, address = {Honolulu, USA}, isbn = {978-1-4244-4761-9}, doi = {10.1109/IGARSS.2010.5649780}, url = {http://hdl.handle.net/2117/11005}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cHaro08, title = {On the non-uniform complexity of brain connectivity}, booktitle = {5th IEEE International Symposium on Biomedical Imaging (ISBI 2008)}, year = {2008}, month = {05/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Paris}, abstract = {

A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides high- dimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensions/complexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.

}, keywords = {biodiffusion, biological tissues, biology computing, brain, brain connectivity, cellular biophysics, cerebral white matter, Clustering methods, complex microstructure, density, Density measurement, Diffusion tensor imaging, Geometry, high-angular resolution diffusion imaging, high-dimensional spaces, High-resolution imaging, Image analysis, Image resolution, Magnetic resonance imaging, manifold learning approach, Microstructure, molecular biophysics, nonuniform complexity, Point processes, Poisson processes, Signal resolution, stratification learning, Switches, Tensile stress, Unsupervised learning}, doi = {10.1109/ISBI.2008.4541139}, author = {Haro, G. and Lenglet, C. and Sapiro, Guillermo and Thompson, P.} } @conference {cPardas00a, title = {A new approach to active contours for tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2000}, isbn = {0-7803-7403-7}, author = {M. Pard{\`a}s and Elisa Sayrol} } @conference {cVidal99, title = {Non-Noticeable Information Embedding in Color Images : Marking and Detection}, booktitle = {33rd ANNUAL - 1999 International Carnahan Conference on Security Technology}, year = {1999}, pages = {293{\textendash}297}, isbn = {0-7803-5247-5}, author = {Vidal, J. and Elisa Sayrol and Cabanillas, S. and Santamaria, S.} } @conference {cTorres95, title = {New approaches to texture coding in segmentation and feature-based image coding schemes}, booktitle = {International Conference on Digital Signal Processing}, year = {1995}, pages = {12{\textendash}17}, isbn = {84-600-7766-7}, author = {Torres, L. and Casas, J.} } @conference {cGimeno94, title = {A new approach to texture coding using stochastic vector quantization}, booktitle = {IEEE International Conference on Image Processing{\textquoteright}94}, year = {1994}, pages = {119{\textendash}123}, abstract = {

A new method for texture coding which combines 2-D linear prediction and stochastic vector quantization is presented in this paper. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ, using an algorithm which takes into account the pixels surrounding the block being encoded. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved

}, isbn = {0-8186-6950-0}, doi = {10.1109/ICIP.1994.413287}, author = {Gimeno, D. and Torres, L. and Casas, J.} } @conference {cBonafonte92, title = {N-best hypotheses:an approach taking into account coarticulation}, booktitle = {EUROPEAN SIGNAL PROCESSING CONFERENCE}, year = {1992}, pages = {379{\textendash}382}, author = {Bonafonte, A. and M. Pard{\`a}s and Mari{\~n}o, J.} } @conference {cGasull92, title = {Non-linear techniques for image interpolation}, booktitle = {VI European Signal Processing Conference}, year = {1992}, pages = {1473{\textendash}1476}, isbn = {0 444 89587 6}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @inbook {bMarques90, title = {NEW GENERATION METHODS FOR HTE HIGH-COMPRESSION CODING OF DIGITAL SEQUENCES}, booktitle = {From pixels to features ii}, year = {1990}, pages = {363{\textendash}375}, isbn = {0444890033}, author = {Marqu{\'e}s, F.} }