Biology subjects

Gonzalez-Garcia, I.

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

3 recordsLinked to original sources

bioRxiv2026

Diurnal rhythmicity modulates self-blaming behaviours and neural processing of autobiographical guilt memories in the trait-like and state-like manner

Increasing evidence implicates late chronotype and higher distinctness as two diurnal dimensions associated with a risk for affective dysregulation. Nonetheless, the underlying behavioural and neural mechanisms remain poorly understood. Previous works in depressive and anxious populations have demonstrated an important role of maladaptive self-blaming processes, with pivotal contributions from superior anterior temporal lobes (sATL), involved in social conceptual knowledge, and subgenual anterior cingulate cortex (sgACC), related to self-worth and social affiliative processing. In the current study, we first tested how diurnal rhythm characteristics were associated with self-blaming emotions and related coping mechanisms through hypothetical social scenarios where participants or their close friends acted towards one another against social norms. Secondly, to probe alterations in the neural correlates of self-blaming, the participants underwent functional neuroimaging during reliving of autobiographical guilt memories. On the behavioural level, late chronotypes demonstrated decreased self-distancing and contextually-maladaptive blaming patterns, i.e. increased blaming of the action recipients rather than the agents. This was paralleled by decreased self-blame-related activity in the sATL and sgACC. Distinctness was associated with altered task-dependent sATL connectivity with hippocampus, brainstem and cerebellum. Importantly, some of the neuroimaging findings reflected interactions between the diurnal rhythm characteristics and time-of-day. As such, the current work provides plausible correlates of adverse emotional outcomes in late chronotypes. Furthermore, it suggests that affective processing reflects an interplay between stable trait-like characteristics and dynamic state-like processes varying across the day, contributing to fine-grained understanding of how emotional cognition is embedded in the brain.

neuroscience↗

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↗