Ciencias Exactas y Ciencias de la Salud

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Deep learning causal study between the gut microbiome composition and autism spectrum disorder manifestation
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Oláguez González, Juan Manuel; Alfaro Ponce, Mariel; emimmayorquin; Sosa Hernández, Víctor Adrián; Breton Deval, Luz de María; Schaeffer, Satu Elisa; School of Engineering and Sciences; Campus Monterrey; Chairez Oria, Jorge Isaac
Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental conditions characterized by early impairments in communication and social interaction, often accompanied by repetitive behaviors. Although its etiology remains unclear, both genetic and environmental factors—including gastrointestinal disturbances—have been implicated. Recent research has highlighted a potential link between ASD and alterations in gut microbiota composition (GMC), with some studies reporting microbial imbalances associated with symptom severity. However, inconsistent methodologies, non-reproducible results, and demographic biases hinder the generalizability of current findings. This thesis investigates the use of machine learning (ML) techniques to model and explore the relationship between gut microbial profiles and ASD. ML offers powerful tools for analyzing complex, nonlinear data across heterogeneous populations, addressing methodological inconsistencies and uncovering patterns that traditional statistical approaches may miss. The objectives of this work are to: (1) identify key microbial predictors of ASD across diverse cohorts, (2) quantify the relative importance of specific bacterial taxa, and (3) simulate simplified microbiota dynamics relevant to ASD. The research was carried out in three stages. First, classical ML algorithms were applied to uncover hidden relationships between microbial profiles and ASD diagnosis, revealing how different bacterial genera may contribute to ASD manifestation in cohorts with diverse GMCs. Second, in silico simulations were performed to visualize the impact of diet on gut microbiota structure and to observe clustering behaviors among bacteria under different dietary regimes. Finally, a semi-supervised model was developed using synthetic data and engineered features, grouping bacteria according to their primary metabolic functions and incorporating these functional categories as novel predictors. In conclusion, focusing on bacterial metabolic functions rather than isolated taxa provides a more robust and interpretable framework for understanding the GMC-ASD relationship, potentially supporting earlier diagnosis and improved insights into the environmental dimensions of ASD.
Deep Learning Approach for Alzheimer’s Disease Classification: Integrating Multimodal MRI and FDG- PET Imaging Through Dual Feature Extractors and Shared Neural Network Processing
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Vega Guzmán, Sergio Eduardo; Alfaro Ponce, Mariel; emimmayorquin; Ochoa Ruíz, Gilberto; Chairez Oria, Jorge Isaac; Hernandez Sanchez, Alejandra; School of Engineering and Sciences; Campus Monterrey; Ramírez Nava, Gerardo Julián
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder whose incidence is expected to grow in the coming years. Traditional diagnostic methods, such as MRI and FDG-PET, each provide valuable but limited insights into the disease’s pathology. This thesis researches the potential of a multimodal deep learning classifier to improve the diagnostic accuracy of AD by integrating MRI and FDG-PET imaging data in comparison to single modality implementations. The study proposes a lightweight neural architecture that uses the strengths of both imaging modalities, aiming to reduce computational costs while maintaining state-of-the-art diagnostic performance. The proposed model utilizes two pre-trained feature extractors, one for each imaging modality, fine-tuned to capture the relevant features from the dataset. The outputs of these extractors are fused into a single vector to form an enriched feature map that better describes the brain. Experimental results demonstrate that the multimodal classifier outperforms single modality classifiers, achieving an overall accuracy of 90% on the test dataset. The VGG19 model was the best feature extractor for both MRI and PET data since it showed superior performance when compared to the other experimental models, with an accuracy of 71.9% for MRI and 80.3% for PET images. The multimodal implementation also exhibited higher precision, recall, and F1 scores than the single-modality implementations. For instance, it achieved a precision of 0.90, recall of 0.94, and F1-score of 0.92 for the AD class and a precision of 0.89, recall of 0.82, and F1-score of 0.86 for the CN class. Furthermore, explainable AI techniques provided insights into the model’s decisionmaking process, revealing that it effectively utilizes both structural and metabolic information to distinguish between AD and cognitively normal (CN) subjects. This research adds supporting evidence into the potential of multimodal imaging and machine learning to enhance early detection and diagnosis of Alzheimer’s disease, offering a cost-effective solution suitable for widespread clinical applications.

Ciencias Exactas y Ciencias de la Salud

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