Browsing by Author "Montalba, Cristian"

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    Interpretable machine learning model for characterizing magnetic susceptibility-based biomarkers in first episode psychosis
    (2025) Franco, Pamela; Montalba, Cristian; Caulier, Raúl; Milovic, Carlos; González, Alfonso; Ramirez, Juan; Undurraga, Juan; Salas, Rodrigo; Crossley, Nicolás; Tejos, Cristian; Uribe, Sergio
    Background and purpose: Several studies have shown changes in neurochemicals within the deep-brain nuclei of patients with psychosis. These alterations indicate a dysfunction in dopamine within subcortical regions affected by fluctuations in iron concentrations. Quantitative Susceptibility Mapping (QSM) is a method employed to measure iron concentration, offering a potential means to identify dopamine dysfunction in these subcortical areas. This study employed a random forest algorithm to predict susceptibility features of the First-Episode Psychosis (FEP) and the response to antipsychotics using Shapley Additionality Explanation (SHAP) values. Methods: 3D multi-echo Gradient Echo (GRE) and T1-weighted GRE were obtained in 61 healthy-volunteers (HV) and 76 FEP patients (32 % Treatment-Resistant Schizophrenia (TRS) and 68 % treatment-Responsive Schizophrenia (RS)) using a 3T Philips Ingenia MRI scanner. QSM and R2* were reconstructed and averaged in twenty-two segmented regions of interest. We used a Sequential Forward Selection as a feature selection algorithm and a Random Forest as a model to predict FEP patients and their response to antipsychotics. We further applied the SHAP framework to identify informative features and their interpretations. Finally, multiple correlation patterns from magnetic susceptibility parameters were extracted using hierarchical clustering. Results: Our approach accurately classifies HV and FEP patients with 76.48 ± 10.73 % accuracy (using four features) and TRS vs RS patients with 76.43 ± 12.57 % accuracy (using four features), using 10-fold stratified cross-validation. The SHAP analyses indicated the top four nonlinear relationships between the selected features. Hierarchical clustering revealed two groups of correlated features for each study. Conclusions: Early prediction of treatment response enables tailored strategies for FEP patients with treatment resistance, ensuring timely and effective interventions.
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    White matter volume and microstructural integrity are associated with fatigue in relapsing multiple sclerosis
    (2025) Figueroa-Vargas, Alejandra; Navarrete-Caro, Sebastián; Cárcamo, Claudia; Ciampi, Ethel; Vásquez-Torres, Macarena; Soler, Bernardita; Montalba, Cristian; Iriarte-Carter, Matías; Martínez-Molina, María Paz; Carvajal-Paredes, Patricio; Ayala-Ochoa, Mariana; Márquez-Rodríguez, Víctor; Figueroa-Taiba, Paulo; Díaz-Díaz, Marcela; Herrero, Joaquín; Henríquez-Ch., Rodrigo; Stecher, Ximena; Manterola, Carla; Zamorano, Francisco; Guevara, Pamela; Aboitiz, Francisco; Billeke, Pablo
    Multiple sclerosis (MS) is a prevalent neurological disorder marked by inflammation and demyelination, with fatigue being one of the most reported and debilitating symptoms. While fatigue occurs across various neurological conditions and even in healthy individuals, the specific mechanisms contributing to fatigue in each context remain unclear. In this study, we conducted a cross-sectional analysis involving 32 people with relapsing MS (PwRMS) and 29 healthy controls who reported fatigue. Participants underwent MRI scans, including T1-weighted and diffusion-weighted imaging. Additionally, the Modified Fatigue Impact Scale was utilized. We employed Bayesian LASSO and Spike-and-Slab LASSO regression models to investigate the hypothesis that fatigue correlates differently with brain structures in PwRMS. Our findings revealed brain regions associated with general and cognitive fatigue. In particular, reduced white matter volume and compromised microstructural integrity in specific areas—such as the cingulate gyrus, inferior frontal gyrus, and the banks of the superior temporal sulcus—showed significant associations with fatigue scores in PwRMS. These results suggest that alterations in specific brain regions may play a critical role in the clinical manifestation of fatigue in MS. Understanding these insights could help differentiate general mechanisms of fatigue from those affecting people with relapsing MS, which may guide future therapeutic strategies.

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