Estudio mediante resonancia magnética funcional y espectroscopía por resonancia magnética del condicionamiento aversivo en pacientes con trastorno obsesivo compulsivo

[spa] Modelos neurobiológicos recientes del trastorno obsesivo-compulsivo (TOC) han sugerido que las alteracions en el condicionamiento del miedo pueden explicar la sintomatología del TOC, y han puesto de relieve la importancia de evaluar las redes cerebrales del miedo. Es importante destacar que la terapia cognitivo-conductual (TCC), que incluye la exposición y la prevención de la respuesta, es una psicoterapia de primera línea para pacientes con TOC y se basa en los principios del condicionamiento del miedo. Se hipotetiza que los pacientes con TOC diferirían de los controles sanos en la conductancia dermal y la activación cerebral durante el condicionamiento del miedo, así como en la concentración de glutamato en la corteza prefrontal ventromedial (vmPFC). Además, la concentración de glutamato en la vmPFC se correlacionaría con la gravedad del TOC y las respuestas de conductancia dermal durante el condicionamiento del miedo. Por último, la activación cerebral y la concentración de glutamato en la vmPFC estarían asociadas con los resultados de la TCC en pacientes con TOC. El objetivo general de esta tesis es evaluar los correlatos neurobiológicos del condicionamiento del miedo en pacientes con TOC, así como su asociación con los resultados de la TCC. En concreto, se compararán la conductancia dermal y la activación cerebral durante el condicionamiento del miedo, junto con la concentración de glutamato en el vmPFC, entre pacientes con TOC y controles sanos. Además, se investigará la relación entre la concentración de glutamato en el vmPFC, la gravedad del TOC y las respuestas de conductancia cutánea durante el condicionamiento del miedo. Por último, se analizarán las medidas cerebrales (es decir, la activación cerebral y la concentración de glutamato en la vmPFC) como posibles biomarcadores predictivos de la respuesta a la TCC en pacientes con TOC. Se evaluraron pacientes con TOC (n = 18) y controles sanos (n = 18) mediante medidas de EDA y fMRI durante un paradigma experimental de condicionamiento del miedo de 2 días (es decir, aprendizaje del miedo, aprendizaje de la extinción del miedo y recuerdo de la extinción del miedo), así como utilizando MRS para evaluar los niveles de glutamato en la vmPFC. Los pacientes con TOC (n = 12) fueron tratados consecutivamente con TCC. Los pacientes con TOC, en comparación con los controles sanos, mostraron una menor activación cerebral en la ínsula y el córtex cingulado anterior dorsal durante el aprendizaje del miedo. La concentración de glutamato en el vmPFC se asoció negativamente con las respuestas de conductancia dermal durante el recuerdo de la extinción del miedo y se correlacionó positivamente con la respuesta a la TCC en pacientes con TOC. Además, la activación cerebral en la ínsula se asoció negativamente con la respuesta a la TCC en pacientes con TOC. En conclusión, el aprendizaje del miedo y el recuerdo de la extinción del miedo deben destacarse como fenómenos psicológicos relevantes relacionados con el TOC y la TCC. En concreto, la concentración de glutamato en el vmPFC parece modular los procesos de recuerdo de la extinción del miedo en pacientes con TOC. Además, la activación cerebral durante el aprendizaje del miedo puede constituir un biomarcador diagnóstico potencial del TOC. Curiosamente, la concentración de glutamato en el vmPFC y la activación cerebral en la ínsula durante el aprendizaje del miedo pueden ayudar a predecir la respuesta a la TCC en pacientes con TOC.[spa] INTRODUCTION: Classical conditioning refers to an associative learning process that occurs when a neutral stimulus (CS) becomes associated with a biologically relevant unconditioned stimulus (US) that elicits an unconditioned response (UR). After multiple CS-US pairings, the CS becomes a conditioned stimulus (CS+) that elicits a conditioned response (CR) qualitatively similar to the UR. When the CS+ is repeatedly reintroduced without being paired with the US, the CS+ without US association is learned, and eventually, the CR declines. This process is called extinction learning. Extinction recall refers to the extinction learning retrieval after a period of time. These same stimuli and phases appear during fear conditioning processes, but with the particularity that the US is characterized by a negative valence that elicits a conditioned fear response. Regarding fear conditioning experimental paradigms, Electro Dermal Activity (EDA), a measure of physical arousal, is the ground truth measurement to evaluate fear conditioning processes in humans. In addition, functional Magnetic Resonance Imaging (fMRI) has recently become an essential neuroscientific tool to investigate fear brain networks during fear conditioning protocols. Likewise, several preclinical studies have established that fear conditioning processes are modulated by glutamatergic fear brain networks. Magnetic Resonance Spectroscopy (MRS), a measure of brain biochemistry in vivo, may allow us to capture the brain glutamatergic modulation of fear conditioning in humans. Recent neurobiological models of obsessive-compulsive disorder (OCD) have suggested that fear conditioning abnormalities may account for OCD symptomatology, and have highlighted the relevance of evaluating fear brain networks. Importantly, cognitive-behavioral therapy (CBT) including exposure and response prevention is a first-line psychotherapy for patients with OCD and is based on the principles of fear conditioning. HYPOTHESES: Patients with OCD would differ from healthy controls in skin conductance and brain activation during fear conditioning, as well as in glutamate concentration in the ventromedial prefrontal cortex (vmPFC). Moreover, vmPFC glutamate concentration would correlate with OCD severity and skin conductance responses during fear conditioning. Finally, brain activation and vmPFC glutamate concentration would be associated with CBT outcome in patients with OCD. AIMS: The overall aim of this thesis is to evaluate the neurobiological correlates of fear conditioning in patients with OCD, as well as their association with CBT outcome. Specifically, skin conductance and brain activation during fear conditioning, along with glutamate concentration in the vmPFC, will be compared between patients with OCD and healthy controls. In addition, the relationship between glutamate concentration in the vmPFC, OCD severity, and skin conductance responses during fear conditioning will be investigated. Lastly, brain measures (i.e., brain activation and vmPFC glutamate concentration) will be analyzed as potential predictive biomarkers of CBT response in patients with OCD

Brain structural alterations in obsessive-compulsive disorder patients with autogenous and reactive obsessions

Obsessive-compulsive disorder (OCD) is a clinically heterogeneous condition. Although structural brain alterations have been consistently reported in OCD, their interaction with particular clinical subtypes deserves further examination. Among other approaches, a two-group classification in patients with autogenous and reactive obsessions has been proposed. The purpose of the present study was to assess, by means of a voxel-based morphometry analysis, the putative brain structural correlates of this classification scheme in OCD patients. Ninety-five OCD patients and 95 healthy controls were recruited. Patients were divided into autogenous (n = 30) and reactive (n = 65) sub-groups. A structural magnetic resonance image was acquired for each participant and pre-processed with SPM8 software to obtain a volume-modulated gray matter map. Whole-brain and voxel-wise comparisons between the study groups were then performed. In comparison to the autogenous group, reactive patients showed larger gray matter volumes in the right Rolandic operculum. When compared to healthy controls, reactive patients showed larger volumes in the putamen (bilaterally), while autogenous patients showed a smaller left anterior temporal lobe. Also in comparison to healthy controls, the right middle temporal gyrus was smaller in both patient subgroups. Our results suggest that autogenous and reactive obsessions depend on partially dissimilar neural substrates. Our findings provide some neurobiological support for this classification scheme and contribute to unraveling the neurobiological basis of clinical heterogeneity in OCD

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging dat

A common brain 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 brain 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 brain network derived from whole brain. We first conducted the anxiety provocation task with healthy participants who tended to feel anxiety related to obsessive-compulsive disorder (OCD) in their daily life. We found a common state anxiety brain network across participants (1585 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants' behavioral trait anxiety scale scores (879 participants from the Human Connectome Project), we demonstrated that trait anxiety shared the same brain network as state anxiety. Furthermore, the brain network between common to state and trait anxiety could detect patients with OCD, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety 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

The T1-dark-rim: A novel imaging sign for detecting smoldering inflammation in multiple sclerosis

Purpose: Paramagnetic rim lesions (PRLs), usually identified in susceptibility-weighted imaging (SWI), are a promising prognostic biomarker of disability progression in multiple sclerosis (MS). However, SWI is not routinely performed in clinical practice. The objective of this study is to define a novel imaging sign, the T1-dark rim, identifiable in a standard 3DT1 gradient-echo inversion-recovery sequence, such as 3D T1 turbo field echo (3DT1FE) and explore its performance as a SWI surrogate to define PRLs. Methods: This observational cross-sectional study analyzed MS patients who underwent 3T magnetic resonance imaging (MRI) including 3DT1TFE and SWI. Rim lesions were evaluated in 3DT1TFE, processed SWI, and SWI phase and categorized as true positive, false positive, or false negative based on the value of the T1-dark rim in predicting SWI phase PRLs. Sensitivity and positive predictive values of the T1-dark rim for detecting PRLs were calculated. Results: Overall, 80 rim lesions were identified in 63 patients (60 in the SWI phase and 78 in 3DT1TFE; 58 true positives, 20 false positives, and two false negatives). The T1-dark rim demonstrated 97% sensitivity and 74% positive predictive value for detecting PRLs. More PRLs were detected in the SWI phase than in processed SWI (60 and 57, respectively). Conclusion: The T1-dark rim sign is a promising and accessible novel imaging marker to detect PRLs whose high sensitivity may enable earlier detection of chronic active lesions to guide MS treatment escalation. The relevance of T1-dark rim lesions that are negative on SWI opens up a new field for analysis

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker

Emotion Regulation and Excess Weight: Impaired Affective Processing Characterized by Dysfunctional Insula Activation and Connectivity

Emotion-regulation strategies are understood to influence food intake. This study examined the neurophysiological underpinnings of negative emotion processing and emotion regulation in individuals with excess weight compared to normal-weight controls. Fifteen participants with excess-weight (body mass index >25) and sixteen normal-weight controls (body mass index 18-25) performed an emotion-regulation task during functional magnetic resonance imaging. Participants were exposed to 24 negative affective or neutral pictures that they were instructed to Observe (neutral pictures), Maintain (sustain the emotion elicited by negative pictures) or Regulate (down-regulate the emotion provoked by negative pictures through previously trained reappraisal techniques). When instructed to regulate negative emotions by means of cognitive reappraisal, participants with excess weight displayed persistently heightened activation in the right anterior insula. Decreased responsivity was also found in right anterior insula, the orbitofrontal cortex and cerebellum during negative emotion experience in participants with excess weight. Psycho-physiological interaction analyses showed that excess-weight participants had decreased negative functional coupling between the right anterior insula and the right dlPFC, and the bilateral dmPFC during cognitive reappraisal. Our findings support contentions that excess weight is linked to an abnormal pattern of neural activation and connectivity during the experience and regulation of negative emotions, with the insula playing a key role in these alterations. We posit that ineffective regulation of emotional states contributes to the acquisition and preservation of excess weight

Trait anxiety is associated with attentional brain networks

Trait anxiety is a well-established risk factor for anxiety and depressive disorders, yet its neural correlates are not clearly understood. In this study, we investigated the neural correlates of trait anxiety in a large sample (n = 179) of individuals who completed the trait and state versions of the State-Trait Anxiety Inventory and underwent resting-state functional magnetic resonance imaging. We used independent component analysis to characterize individual resting-state networks (RSNs), and multiple regression analyses to assess the relationship between trait anxiety and intrinsic connectivity. Trait anxiety was significantly associated with intrinsic connectivity in different regions of three RSNs (dorsal attention network, default mode network, and auditory network) when controlling for state anxiety. These RSNs primarily support attentional processes. Notably, when state anxiety was not controlled for, a different pattern of results emerged, highlighting the importance of considering this factor in assessing the neural correlates of trait anxiety. Our findings suggest that trait anxiety is uniquely associated with resting-state brain connectivity in networks mainly supporting attentional processes. Moreover, controlling for state anxiety is crucial when assessing the neural correlates of trait anxiety. These insights may help refine current neurobiological models of anxiety and identify potential targets for neurobiologically-based interventions

Neurogenetics of Dynamic Connectivity Patterns Associated With Obsessive-Compulsive Symptoms in Healthy Children

Background: Obsessive-compulsive symptoms (OCSs) during childhood predispose to obsessive-compulsive disorder and have been associated with changes in brain circuits altered in obsessive-compulsive disorder samples. OCSs may arise from disturbed glutamatergic neurotransmission, impairing cognitive oscillations and promoting overstable functional states. Methods: A total of 227 healthy children completed the Obsessive Compulsive Inventory-Child Version and underwent a resting-state functional magnetic resonance imaging examination. Genome-wide data were obtained from 149 of them. We used a graph theory-based approach and characterized associations between OCSs and dynamic functional connectivity (dFC). dFC evaluates fluctuations over time in FC between brain regions, which allows characterizing regions with stable connectivity patterns (attractors). We then compared the spatial similarity between OCS-dFC correlation maps and mappings of genetic expression across brain regions to identify genes potentially associated with connectivity changes. In post hoc analyses, we investigated which specific single nucleotide polymorphisms of these genes moderated the association between OCSs and patterns of dFC. Results: OCSs correlated with decreased attractor properties in the left ventral putamen and increased attractor properties in (pre)motor areas and the left hippocampus. At the specific symptom level, increased attractor properties in the right superior parietal cortex correlated with ordering symptoms. In the hippocampus, we identified two single nucleotide polymorphisms in glutamatergic neurotransmission genes (GRM7, GNAQ) that moderated the association between OCSs and attractor features. Conclusions: We provide evidence that in healthy children, the association between dFC changes and OCSs may be mapped onto brain circuits predicted by prevailing neurobiological models of obsessive-compulsive disorder. Moreover, our findings support the involvement of glutamatergic neurotransmission in such brain network changes