Biology subjects

Gonzalez-Garcia, I.

Publications and source records attributed to Gonzalez-Garcia, I..

2 recordsLinked to original sources

bioRxiv2025

Subclinical anxiety is associated with reduced self-distancing and enhanced guilt-related connectivity between anterior temporal and subgenual cingulate cortices

Excessive self-blaming emotions are commonly observed in anxiety disorders, with qualitatively similar symptomatology reported in subclinical populations. Interpretation of moral information requires assessing the social conceptual information, a process overseen by the superior anterior temporal lobe (sATL). Feelings of self-blame evoke interactions of sATL and socio-affective regions, and previous research shows that subclinical anxiety modulates the organisation of the self-blame circuitry. This study aimed to extend these findings by exploring links of trait-anxiety with (i) self-blaming emotions and associated behaviours in an experimental task, and (ii) self-blame-dependent neural activity and connectivity, as observed during reliving of autobiographical guilt memories. We also explored the role of resting-state fMRI in linking these phenomena. Increased anxiety was linked to stronger self-blaming emotions, and more pronounced self-attacking and hiding. When experiencing negative emotions about themselves (i.e. shame and self-anger), anxious individuals were also less likely to disengage from self-focused thoughts. These behavioural findings were paralleled by enhanced self-blame-related connectivity between the left sATL and bilateral posterior subgenual cingulate cortex. Distinct patterns of activity and connectivity within the ATL-related circuitry were furthermore linked to individual differences in intensity of the self-blaming emotions and approach-avoidance motivation towards the guilt memories. As such, the results of the current study link stronger self-blaming emotions in anxious individuals with specific maladaptive patterns of behaviour. Furthermore, the work provides robust evidence for the important role of ATL-related circuitry in self-blame processing, supporting its broader involvement in social conceptual processing and its alterations in subclinical anxiety.

neuroscience↗

bioRxiv2024

Towards a Survival Risk Prediction Model for Metastatic NSCLC Patients on Durvalumab Using Whole-Lung CT Radiomics

BackgroundExisting criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting less-than-median-survival risk in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. MethodsTo ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. ResultsThe classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. ConclusionMachine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

bioinformatics↗