Browsing by Author "Ramirez Mahaluf, Juan Pablo"

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    Functional Dysconnectivity in Ventral Striatocortical Systems in 22q11.2 Deletion Syndrome
    (OXFORD UNIV PRESS, 2021) Tepper, Angeles; Cuiza Vasquez Analia; Alliende, Luz María; Mena, Carlos; Ramirez Mahaluf, Juan Pablo; Iruretagoyena, Barbara; Ornstein, Claudia; Fritsch, Rosemarie; Nachar, Ruben; Gonzalez Valderrama, Alfonso; Undurraga, Juan; Pablo Cruz, Juan; Tejos, Cristian; Fornito, Alex; Repetto, Gabriela; Crossley, Nicolas
    22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental disorder that represents one of the greatest known risk factors for psychosis. Previous studies in psychotic subjects without the deletion have identified a dopaminergic dysfunction in striatal regions, and dysconnectivity of striatocortical systems, as an important mechanism in the emergence of psychosis. Here, we used resting-state functional MRI to examine striatocortical functional connectivity in 22q11.2DS patients. We used a 2 x 2 factorial design including 125 subjects (55 healthy controls, 28 22q11.2DS patients without a history of psychosis, 10 22q11.2DS patients with a history of psychosis, and 32 subjects with a history of psychosis without the deletion), allowing us to identify network effects related to the deletion and to the presence of psychosis. In line with previous results from psychotic patients without 22q11.2DS, we found that there was a dorsal to ventral gradient of hypo- to hyperstriatocortical connectivity related to psychosis across both patient groups. The 22q11.2DS was additionally associated with abnormal functional connectivity in ventral striatocortical networks, with no significant differences identified in the dorsal system. Abnormalities in the ventral striatocortical system observed in these individuals with high genetic risk to psychosis may thus reflect a marker of illness risk.
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    Interpretable Machine Learning Model for Characterizing Magnetic Susceptibility based Biomarkers in First Episode Psychosis
    (2025) Franco, Pamela ; Montalba Zalaquett, Cristian Andres; Caulier-Cisterna, Raul; Milovic Fabregat, Carlos Andrés; Gonzalez, Alfonso ; Ramirez Mahaluf, Juan Pablo; Undurraga, Juan ; Salas, Rodrigo ; Crossley, Nicolás; Tejos Núñez, Cristián Andrés; Uribe, Sergio
    Altered neurochemicals in deep-brain nuclei, especially dopamine dysfunction, arelinked to psychosis. Quantitative Susceptibility Mapping (QSM) measures brainmagnetic susceptibility changes, including iron concentration, which affects dopaminepathways. This study used machine learning (ML) to analyze MRI data and build aclassifier distinguishing healthy individuals from First-Episode Psychosis (FEP)patients while predicting their response to antipsychotic treatment. A random forestmodel was trained, with the SHAP framework assessing feature importance andinterpretability. Hierarchical clustering identified relationships among features. Themodel achieved performance, with 76.48 ± 10.73% accuracy for classifying FEPpatients (based on R2* values in the nucleus accumbens and amygdala, and QSM inthe thalamus) and 76.43 ± 12.57% accuracy for predicting treatment response (basedon R2* values in the hippocampus, caudate, and putamen, and QSM in the amygdala).MRI-based biomarkers and ML could help tailor personalized treatments for FEPpatients, especially those not responding to standard therapies.