Alvaro Ulloa

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Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between(More)
The association of copy number variation (CNV) with schizophrenia has been reported with evidence of increased frequency of both rare and large CNVs. Yet, little is known about the impact of CNVs in brain structure. In this pilot study, we explored collective effects of all CNVs in each cytogenetic band on the risk of schizophrenia and gray matter variation(More)
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based(More)
High data dimensionality poses a major challenge for imaging genomic studies. To address this issue, a semi-blind multivariate approach, parallel independent component analysis with multiple references (pICA-MR), is proposed. pICA-MR extracts imaging and genetic components in parallel and enhances inter-modality correlations. Prior knowledge is incorporated(More)
Deep learning methods have significantly improved classification accuracy in different areas such as speech, object and text recognition. However, this field has only began to be explored in the brain imaging field, which differs from other fields in terms of the amount of data available, its data dimensionality and other factors. This paper proposes a(More)
In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging(More)
Spatially-varying signal content can be effectively modeled using amplitude modulation-frequency modulation (AM-FM) representations. The AM-FM representation allow us to extract instantaneous amplitude (IA) and instantaneous frequency (IF) components that can be used to measure non-stationary content in biomedical images and videos. This paper introduces a(More)
Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by deep learning approaches, but they still may require significant amounts of data. In this paper, we seek to(More)
There is a growing interest in identifying neuroimaging-based biomarkers for schizophrenia. Previous studies have shown both functional and structural brain abnormalities in schizophrenia patients. One main category of these findings consists of volumetric abnormalities in brain structure in different cortical and subcortical structures in patients' brain.(More)
BACKGROUND Copy number variations (CNVs) are structural genetic mutations consisting of segmental gains or losses in DNA sequence. Although CNVs contribute substantially to genomic variation, few genetic and imaging studies report association of CNVs with alcohol dependence (AD). Our purpose is to find evidence of this association across ethnic populations(More)