Biology subjects

Soriano-Mas, C.

Publications and source records attributed to Soriano-Mas, C..

7 recordsLinked to original sources

bioRxiv2025

Inhibitory control and error processing in Obsessive-Compulsive Disorder: A mega-analysis of task-based fMRI data by the ENIGMA-OCD consortium

ObjectiveObsessive-compulsive disorder (OCD) is a chronic condition in which impaired inhibitory control and excessive error monitoring may contribute to the maintenance of obsessions and compulsions. This mega-analysis investigates neural activation during response inhibition and error processing using adult and pediatric data from the ENIGMA-OCD consortium and the ABCD study. MethodsIndividual participant data was uniformly processed using HALFpipe to extract statistical maps for response inhibition and error processing contrasts. Bayesian multilevel models were used to assess regional and whole-brain effects of OCD, with additional analyses examining the association between the OCD clinical profile and task-related activation. ResultsAcross inhibitory control tasks, both individuals with OCD and control participants showed robust activation in regions implicated in response inhibition and error processing. During response inhibition, compared to controls, adults with OCD showed stronger somatomotor cortex activation, while children with OCD showed stronger occipital cortex activation. Children with likely OCD from the ABCD cohort showed reduced activity in the frontoparietal network in the anterior insula/frontal operculum region. During error processing, relative to controls, adults with OCD showed weaker activation in fronto-striatal regions, while children with OCD showed stronger activation in frontoparietal and attention networks. Greater OCD symptom severity was associated with weaker task-related activation in adults and stronger activation in children during response inhibition. ConclusionCase-control differences in brain activation during inhibitory control varied by age group and task contrast. Symptom severity emerged as the main clinical correlate of activation during inhibition, suggesting that inhibitory control deficits in OCD may be both state-dependent and developmentally specific.

neuroscience↗

bioRxiv2025

Executive control in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium

ObjectiveObsessive-compulsive disorder (OCD) is associated with impaired executive function and altered activity in associated neural circuits, contributing to reduced goal-directed behavior. To investigate neural activation during executive control, we conducted a mega-analysis in the ENIGMA-OCD consortium pooling individual participant data from 475 individuals with OCD and 345 healthy controls across 15 fMRI tasks collected worldwide. MethodsIndividual participant data was uniformly processed using HALFpipe to construct voxelwise statistical images of executive control and task load contrasts. Parameter estimates extracted from regions of interest were entered into multilevel Bayesian models to examine regional and whole-brain effects of diagnosis, and, within OCD, the influence of medication status, symptom severity, and age of onset on task activation. ResultsWe observed a robust task activation pattern across individuals with OCD and control participants in executive control regions across tasks. Relative to controls, individuals with OCD showed moderate to very strong evidence of weaker activation of the dorsolateral prefrontal cortex, precuneus, frontal eye fields, and inferior parietal lobule during executive control (all positive posterior probabilities [P+]<0.1). Individuals with OCD also showed stronger activation in regions of the default mode network during executive function relative to controls. We found little evidence for differential activation during executive control in task- positive regions related to disease onset, severity and medication status. ConclusionIn the first mega-analysis of fMRI studies of executive function in OCD, we found strong evidence of weaker frontoparietal activation during executive control tasks. Our findings also suggest a failure of default mode network regions to appropriately disengage during task performance in OCD.

neuroscience↗

bioRxiv2024

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

bioRxiv2023

Hippocampal and amygdala subfield volumes in obsessive-compulsive disorder differ according to medication status.

IntroAlthough it has been suggested that the hippocampus and amygdala (HA) are involved in the neurobiology of obsessive-compulsive disorder (OCD), volumetric findings have been inconsistent. Furthermore, the HA consist of heterogenous anatomic units with specific functions and cytoarchitecture, and little work has been undertaken on the volumetry of these subfields in OCD. MethodsT1-weighted images from 381 patients with OCD and 338 healthy controls (HCs) from the OCD Brain Imaging Consortium were segmented to produce twelve hippocampal subfields and nine amygdala subfields using FreeSurfer 6.0. We assessed between-group differences in subfield volume using a mixed-effects model adjusted for age, quadratic effects of age, sex, site, and whole HA volume. Given evidence of confounding effects of clinical characteristics on brain volumes in OCD, we also performed subgroup analyses to examine subfield volume in relation to comorbid anxiety and depression, medication status, and symptom severity. ResultsPatients with OCD and HCs did not significantly differ in HA subfield volume. However, medicated patients with OCD had significantly smaller hippocampal dentate gyrus (pFDR=0.042, d=-0.26) and molecular layer (pFDR=0.042, d=-0.29) and larger lateral (pFDR=0.049, d=0.23) and basal (pFDR=0.049, d=0.25) amygdala subfields than HCs. Unmedicated patients had significantly smaller hippocampal CA1 (pFDR=0.016, d=-0.28) than HCs. No association was detected between any subfield volume and OCD severity. ConclusionDifferences in HA subfields between OCD and HCs are dependent on medication status, in line with previous work on other brain volumetric alterations in OCD. This emphasizes the importance of considering psychotropic medication in neuroimaging studies of OCD.

neuroscience↗

bioRxiv2023

Computational modeling of reinforcement learning and functional neuroimaging of probabilistic reversal dissociates compulsive behaviors in Gambling and Cocaine Use Disorders

Cognitive flexibility refers to the ability to adjust to changes in the environment and is essential for adaptive behavior. It can be investigated using laboratory tests such as probabilistic reversal learning (PRL). In individuals with both Cocaine Use Disorder (CUD) and Gambling Disorder (GD), overall impairments in PRL flexibility are observed. However, it is poorly understood whether this impairment depends on the same brain mechanisms in cocaine and gambling addictions. Reinforcement learning (RL) is the process by which rewarding or punishing feedback from the environment is used to adjust behavior, to maximise reward and minimise punishment. Using RL models, a deeper mechanistic explanation of the latent processes underlying cognitive flexibility can be gained. Here, we report results from a re-analysis of PRL data from control participants (n=18) and individuals with either GD (n=18) or CUD (n=20) using a hierarchical Bayesian RL approach. We observed significantly reduced stimulus stickiness (i.e., stimulus-bound perseveration) in GD, which may reflect increased exploratory behavior that is insensitive to outcomes. RL parameters were unaffected in CUD. We relate the behavioral findings to their underlying neural substrates through an analysis of task-based fMRI data. We report differences in tracking reward and punishment expected values (EV) in individuals with GD compared to controls, with greater activity during reward EV tracking in the cingulate gyrus and amygdala. In CUD, we observed reduced responses to positive punishment prediction errors (PPE) and increased activity following negative PPEs in the superior frontal gyrus compared to controls. Thus, an RL framework serves to differentiate behavior in a probabilistic learning paradigm in two compulsive disorders, GD and CUD.

neuroscience↗

bioRxiv2020

Neurophysiological signatures in the retrieval of individual autobiographical memories of real-life episodic events

Autobiographical memory (AM) refers to recollected events that belong to an individuals past. In a classical episodic retrieval experiment in a laboratory, the events to be remembered are words or pictures that have hardly any personal relevance. While such stimuli provide necessary experimental and controlled conditions helping to advance in the understanding of memory, they do not capture the whole complexity of real-world stimuli. Recently, the incorporation of wearable cameras has allowed us to study the cognitive and neural bases of AM retrieval without active participant involvement, and they have been demonstrated to elicit a strong sense of first-person episodic recollection enhancing ecological validity. Here, we provide a new approach to understanding the retrieval of personal events, implementing a convolution network-based algorithm for the selection of the stimuli while monitoring participants memory retrieval with scalp EEG recordings over three periods of time after encoding (1 week, 2 weeks, and 6 to 12 months). We also examined an individual with a condition termed Aphantasia that provided more insights into the sensitivity of our protocol in the investigation of individual AM using real-life sequences.

neuroscience↗

bioRxiv2017

A common neural network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a neural network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related neural network derived from whole brain. Through the fMRI task for provoking anxiety, we found a common neural network of state anxiety across participants (1,638 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants trait anxiety scale (879 participants from the Human Connectome Project), we demonstrated that trait anxiety also shared the same neural network as state anxiety. Furthermore, the common neural network between state and trait anxiety could detect patients with obsessive-compulsive disorder, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety are not completely independent but have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

neuroscience↗