Deactivation Patterns Research Articles

The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention tool for the noninvasive modulation of brain activity and behavior in neuroscience research and clinical settings. However, the resting-state dynamic evolution of large-scale functional brain networks following rTMS has rarely been investigated. Here, using resting-state fMRI images collected from 23 healthy individuals before (baseline) and after 1 Hz rTMS of the left frontal (FRO) and occipital (OCC) lobes, we examined the different effects of rTMS on brain dynamics across the human cortex. By fitting a pairwise maximum entropy model (pMEM), we constructed an energy landscape for the baseline and poststimulus conditions by fitting a pMEM. We defined dominant brain states (local minima) in the energy landscape with synergistic activation and deactivation patterns of large-scale functional networks. We calculated state dynamics including appearance probability, transitions and duration. The results showed that 1 Hz rTMS induced increased and decreased state probability, transitions and duration when delivered to the FRO and OCC targets, respectively. Most importantly, the shortest path and minimum cost between dominant brain states were altered after stimulation. The absolute sum of the costs from the source states to the destinations was lower after OCC stimulation than after FRO stimulation. In conclusion, our study characterized the dynamic trajectory of state transitions in the energy landscape and suggested that local rTMS can induce significant dynamic perturbation involving stimulated and distant functional networks, which aligns with the modern view of the dynamic and complex brain. Our results suggest low-dimensional mapping of rTMS-induced brain adaption, which will contribute to a broader and more effective application of rTMS in clinical settings.

Ageing-Related Changes to H3K4me3, H3K27ac, and H3K27me3 in Purified Mouse Neurons.

Neurons are central to lifelong learning and memory, but ageing disrupts their morphology and function, leading to cognitive decline. Although epigenetic mechanisms are known to play crucial roles in learning and memory, neuron-specific genome-wide epigenetic maps into old age remain scarce, often being limited to whole-brain homogenates and confounded by glial cells. Here, we mapped H3K4me3, H3K27ac, and H3K27me3 in mouse neurons across their lifespan. This revealed stable H3K4me3 and global losses of H3K27ac and H3K27me3 into old age. We observed patterns of synaptic function gene deactivation, regulated through the loss of the active mark H3K27ac, but not H3K4me3. Alongside this, embryonic development loci lost repressive H3K27me3 in old age. This suggests a loss of a highly refined neuronal cellular identity linked to global chromatin reconfiguration. Collectively, these findings indicate a key role for epigenetic regulation in neurons that is inextricably linked with ageing.

Deactivation Patterns of Potassium-Based γ-Alumina Dry Sorbents for CO2 Capture

Gamma-alumina (γ-Al2O3) is an essential support material in dry sorbents used to capture CO2 from flue gas. This study explores the deactivation of potassium-based γ-Al2O3 sorbents due to by-products such as KAl(CO3)(OH)2 during CO2 capture. We synthesized sorbents with K2CO3 loadings of 5, 10, 20, and 30 wt% and subjected them to repeated capture and regeneration cycles. The results show significant variations in the deactivation degree: the sorbent with 5 wt% K2CO3 exhibited a 100% deactivation rate, while the 30 wt% variant showed a markedly reduced rate of 44.6%. These findings highlight the impact of the formation of KAl(CO3)(OH)2 at the interface between K2CO3 and γ-Al2O3 on sorbent deactivation. An equation that can be used to predict the final CO2 capture capacity based on the ratio of active material to support was proposed using these results.

Altered Cortical Activity during a Finger Tap in People with Stroke.

This study describes electroencephalography (EEG) measurements during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13-26Hz) of people with chronic stroke (N = 10, 7F/3M) and controls (N = 10, 7F/3M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and the change in task-based phase-locking value (tbPLV) and beta band task-based coherence (tbCoh) relative to baseline coherence as connectivity between EEG electrodes. Finger movements were associated with a decrease in beta power (event related desynchronization (ERD)) followed by an increase in beta power (event related resynchronization (ERS)). The ERS in the post task period was lower in the stroke group (7%), compared to controls (44%) (p < 0.001) and the transition from ERD to ERS was delayed in the stroke group (1.43s) compared to controls (0.90s) in the C3 electrode (p = 0.007). In the same post movement period, the stroke group maintained a heightened tbPLV (p = 0.030 for time to baseline of the C3:Fz electrode pair) and did not show the decrease in connectivity in electrode pair C3:Fz that was observed in controls (tbPLV: p = 0.006; tbCoh: p = 0.023). Our results suggest that delays in cortical deactivation patterns following movement coupled with changes in the time course of connectivity between the sensorimotor and frontal cortices in the stroke group might explain clinical observations of prolonged muscle activation in people with stroke. This prolonged activation might be attributed to the combination of cortical reorganization and changes to sensory feedback post-stroke.

Prediction of catalytic cracking performance during co-processing of vacuum gas oil and low-margin oil refining streams

Relevance. Expansion of catalytic cracking feedstock resources both due to the need to intensify the process to increase the yield of target products (high-octane gasoline, light olefins) and to deepen oil refining through the utilization of low-margin streams at refineries. Along with this, there is an urgent need to develop domestic mathematical tools for optimizing the catalytic cracking, predicting the process performance when the modes and feedstock qualities changes, as well as planning of production. This requires in-depth analysis and detailed study of the composition of oil fractions involved in processing and thermodynamics and kinetics of a heterogeneous process. The development and application of a mathematical model of the catalytic cracking, taking into account the composition and properties of the components involved in processing, makes it possible to quantitatively assess the yield and quality of target and by-products depending on the composition, physicochemical properties of the mixed feedstock, and the parameters of the technological regime, with an assessment of the possibility of their processing at an existing industrial facility. Aim. Experimental study of the composition and properties of mixed feedstock of catalytic cracking based on vacuum gas oil containing 5 to 20 wt % of extract of selective cleaning of oils, distillate, residual slack wax, and deasphalted oil, and prediction of the catalytic cracking indicators during their co-processing using a mathematical model. Methods. Liquid chromatography method to study the composition of feedstock materials of the catalytic cracking in combination with a number of standard methods for determining physico-chemical properties. Results. Using a set of experimental studies, the authors have established the patterns of changes in the composition and physico-chemical properties of the components and mixed feedstock of catalytic cracking containing 5–20 wt % of distillate and residual slack wax, deasphalted oil, and extract. The results obtained were used in development of a mathematical model of the heterogeneous catalytic cracking of feedstock, which takes into account the composition of oil fractions involved in processing and the patterns of catalyst deactivation by coke. Using a mathematical model, the authors established the patterns of changes in the composition and yield of process products when 5–20 wt % were involved in processing distillate slack wax and extracts of selective cleaning of oils mixed with vacuum distillate. Practical recommendations were developed on the possibility of expanding the hydrocarbon feedstock of the catalytic cracking, taking into account the fuel or petrochemical regime.

Unlocking the Mysteries of Technical Catalyst Deactivation: A View from Space

AbstractModern analytical techniques enable researchers to study heterogeneous catalytic systems at extended length scales and with local probing methods which were previously impractical. Such spatially–resolved analyses are ideal for exploring the complex dynamics governing catalytic activity, and more specifically, deactivation. Here we highlight significant experimental concepts and milestones in the spatially–resolved analysis of technical catalysts, where it is now possible to study catalyst behavior even up to industrially relevant scale. At such extended length scales and in contrast to many model systems, spatial heterogeneities in solid catalyst bodies may play a crucial role in controlling catalytic properties and may be closely linked to catalyst deactivation. Spatially–resolved studies can therefore provide a unique source of information about such local phenomena. Researchers can gain a deeper insight into the operational life of a catalyst by understanding deactivation patterns, which are one of many factors influencing the dynamics of catalytic reactions. In turn, this information promotes the design of more robust and sustainable catalytic systems. We therefore outline the current state of spatially–resolved characterization, together with its role in deconvoluting the complexity of technical catalysts and their deactivation.

Emotional dysregulation and stimulant medication in adult ADHD.

Emotional dysregulation affects up to two-thirds of adult patients with attention-deficit/hyperactivity disorder (ADHD) and is increasingly seen as a core ADHD symptom that is clinically associated with greater functional impairment and psychiatric comorbidity. We sought to investigate emotional dysregulation in ADHD and explored its neural underpinnings. We studied emotion induction and regulation in a clinical cohort of adult patients with ADHD before and after a stimulant challenge. We compared patients with age- and gender-matched healthy controls using behavioural, structural, and functional measures. We hypothesized that patients would demonstrate aberrant emotion processing compared with healthy controls, and sought to find whether this could be normalized by stimulant medication. Behaviourally, the ADHD group showed reduced emotion induction and regulation capacity. Brain imaging revealed abberant activation and deactivation patterns during emotion regulation, lower grey-matter volume in limbic and paralimbic areas, and greater grey-matter volume in visual and cerebellar areas, compared with healthy controls. The behavioural and functional deficits seen in emotion induction and regulation in the ADHD group were not normalized by stimulant medication. Patients with ADHD may have impaired emotion induction and emotion regulation capacity, but these deficits are not reversed by stimulant medication. These results have important clinical implications when assessing which aspects of emotional dysregulation are relevant for patients and if and how traditional ADHD pharmacotherapy affects emotion induction and emotion regulation.

Identification of cognitive load-dependent activation patterns using working memory task-based fMRI at various levels of difficulty

Working memory, which is regarded as the foundation of cognitive processes, is a system that stores, processes, and manipulates information in short intervals of time that are actually needed for daily functioning. This study aimed to assess the brain activity of healthy controls (HC) while performing the N-back task, which is one of the most popularly used tests for evaluating working memory along with functional magnetic resonance imaging (fMRI). In this regard, we collected fMRI data from right-handed individuals in a 3.0 T scanner during the Persian version of the visual variant N-back task performance with three levels of complexity varied throughout the experiment (1, 2, and 3-back conditions) to increase the cognitive demands. The statistical parametric mapping (SPM12) software was used to analyze fMRI data for the identification of cognitive load-dependent activation patterns based on contrast images obtained from different levels of task difficulty. Our findings showed that as cognitive complexity increased, the number of significant activation clusters and cluster extent increased in several areas distributed in the cerebellum, frontoparietal lobes, insula, SMA, and lenticular nucleus, the majority of which are recognized for their role in working memory. Furthermore, deactivation patterns during 1-, 2-, and 3-back vs. 0-back contrasts revealed significant clusters in brain regions that are mostly described as being part of the default mode network (DMN). Based on previous research, our results supported the recognized involvement of the mentioned cortical and subcortical areas in various types or levels of N-back tasks. This study found that altering activation patterns by increasing task difficulty could aid in evaluating the various stages of cognitive dysfunction in many brain diseases such as multiple sclerosis (MS) and Alzheimer's disease by comparing controls in future studies to apply early appropriate treatment strategies.

Group independent components underpin responses to items from a depression scale.

The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales. In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC. Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.

Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest

Previous studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodological framework. In this study, by starting from pair-wise assessments of instantaneous phase synchronization and community membership, we assemble spatiotemporally flexible networks that reflect changes in integration/segregation that occur at a spectrum of spatial as well as temporal scales. This is achieved by iteratively assembling smaller networks into larger units under the constraint that the smaller units should be internally integrated, i.e. belong to the same community. The assembled subnetworks can be partly overlapping and differ in size across time. Our results show that subnetwork integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise that are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain's functional connectome is a presence of quasi-periodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. Additionally, we show that the degree of network integration throughout a n-back working memory task is correlated to performance.

Transcriptional Activation, Deactivation and Rebound Patterns in Cortex, Hippocampus and Amygdala in Response to Ketamine Infusion in Rats.

Ketamine, an N-methyl-D-aspartate (NMDA)-receptor antagonist, is a recently revitalized treatment for pain and depression, yet its actions at the molecular level remain incompletely defined. In this molecular-pharmacological investigation in the rat, we used short- and longer-term infusions of high dose ketamine to stimulate neuronal transcription processes. We hypothesized that a progressively stronger modulation of neuronal gene networks would occur over time in cortical and limbic pathways. A continuous intravenous administration paradigm for ketamine was developed in rat consisting of short (1 h) and long duration (10 h, and 10 h + 24 h recovery) infusions of anesthetic concentrations to activate or inhibit gene transcription in a pharmacokinetically controlled fashion. Transcription was measured by RNA-Seq in three brain regions: frontal cortex, hippocampus, and amygdala. Cellular level gene localization was performed with multiplex fluorescent in situ hybridization. Induction of a shared transcriptional regulatory network occurred within 1 h in all three brain regions consisting of (a) genes involved in stimulus-transcription factor coupling that are induced during altered synaptic activity (immediate early genes, IEGs, such as c-Fos, 9–12 significant genes per brain region, p < 0.01 per gene) and (b) the Nrf2 oxidative stress-antioxidant response pathway downstream from glutamate signaling (Nuclear Factor Erythroid-Derived 2-Like 2) containing 12–25 increasing genes (p < 0.01) per brain region. By 10 h of infusion, the acute results were further reinforced and consisted of more and stronger gene alterations reflecting a sustained and accentuated ketamine modulation of regional excitation and plasticity. At the cellular level, in situ hybridization localized up-regulation of the plasticity-associated gene Bdnf, and the transcription factors Nr4a1 and Fos, in cortical layers III and V. After 24 h recovery, we observed overshoot of transcriptional processes rather than a smooth return to homeostasis suggesting an oscillation of plasticity occurs during the transition to a new phase of neuronal regulation. These data elucidate critical molecular regulatory actions during and downstream of ketamine administration that may contribute to the unique drug actions of this anesthetic agent. These molecular investigations point to pathways linked to therapeutically useful attributes of ketamine.

Task-specific network interactions across key cognitive domains.

Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.

A Model of Catalytic Cracking: Catalyst Deactivation Induced by Feedstock and Process Variables

Changes in the quality of the feedstocks generated by involving various petroleum fractions in catalytic cracking significantly affect catalyst deactivation, which stems from coke formed on the catalyst surface. By conducting experimental studies on feedstocks and catalysts, as well as using industrial data, we studied how the content of saturates, aromatics and resins (SAR) in feedstock and the main process variables, including temperature, consumptions of the feedstock, catalyst and slops, influence the formation of catalytic coke. We also determined catalyst deactivation patterns using TG-DTA, N2 adsorption and TPD, which were further used as a basis for a kinetic model of catalytic cracking. This model helps predict the changes in reactions rates caused by coke formation and, also, evaluates quantitatively how group characteristics of the feedstock, the catalyst-to-oil ratio and slop flow influence the coke content on the catalyst and the degree of catalyst deactivation. We defined that a total loss of acidity changes from 8.6 to 30.4 wt% for spent catalysts, and this depends on SAR content in feedstock and process variables. The results show that despite enriching the feedstock by saturates, the highest coke yields (4.6–5.2 wt%) may be produced due to the high content of resins (2.1–3.5 wt%).

Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial

BackgroundNeural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions. MethodsA prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers. ResultsDysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker ConclusionsThese findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.

Functional brain network community structure in childhood: Unfinished territories and fuzzy boundaries

Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children’s brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9–11 years (48% female, replication sample n=544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood.

An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change.

Recent functional neuroimaging studies suggest that the brain networks responsible for time processing are involved during other cognitive processes, leading to a hypothesis that time-related processing is needed to perform a range of tasks across various cognitive functions. To examine this hypothesis, we analyze whether, in healthy subjects, the brain structures activated or deactivated during performance of timing and oddball-detection type tasks coincide. To this end, we conducted two independent signed differential mapping (SDM) meta-analyses of functional magnetic resonance imaging (fMRI) studies assessing the cerebral generators of the responses elicited by tasks based on timing and oddball-detection paradigms. Finally, we undertook a multimodal meta-analysis to detect brain regions common to the findings of the two previous meta-analyses. We found that healthy subjects showed significant activation in cortical areas related to timing and salience networks. The patterns of activation and deactivation corresponding to each task type partially coincided. We hypothesize that there exists a time and change-detection network that serves as a common underlying resource used in a broad range of cognitive processes.

Tetralin Hydrogenation over Supported Nickel Catalysts

An investigation was carried out to study the feasibility of using 5% nickel/alumina catalysts synthesized with different methods and metal precursors for the hydrogenation of tetralin. The catalysts were characterized using atomic absorption spectroscopy, thermogravimetric analysis, X-ray powder diffraction, X-ray photoelectron spectroscopy (XPS), hydrogen chemisorption, and surface area and pore volume analyses. The catalysts were tested in a continuous-flow, fixed-bed, reactor using solvent-free tetralin feed under the conditions of 5 barg hydrogen, 210 °C, a weight hourly space velocity of 5 h–1, and a H2/HC ratio of 7.5 (gas hourly space velocity, 4220 h–1). These reaction conditions were much milder than the ones reported in literature but showed considerably higher catalytic activity even with low metal loadings. The study indicated that the product’s isomer ratio was governed by kinetics. Indeed, a remarkable difference was observed in the selectivity for the catalysts synthesized with different metal precursors in relation to cis/trans decalin ratios, where catalysts derived from nickel carbonate showed higher selectivity to cis-decalin compared to catalysts prepared from nickel nitrate. It was speculated that the dispersion and particle size played an important role in this variance, as suggested by XPS and hydrogen chemisorption. It was also found that this change in selectivity was not related to conversion. Similarly, different deactivation patterns were observed over these catalysts.

Differential Patterns of Activation and Deactivation among Brain Regions Selective for Face and Scene Processing

Differential Patterns of Activation and Deactivation among Brain Regions Selective for Face and Scene Processing

Activation and deactivation patterns in schizophrenia during performance of an fMRI adapted version of the stroop task

The Stroop task, which examines an aspect of executive function/cognitive control, the ability to inhibit prepotent responses, has been relatively little examined in schizophrenia, and the findings have been inconsistent. Whether performance of this task is associated with failure of de-activation in the disorder is also uncertain. We examined 42 schizophrenic patients and 61 healthy controls during performance of an fMRI-adapted version of the Stroop task, the counting Stroop task. Task-related activations (incongruent > congruent condition) and de-activations (baseline > incongruent) were examined using whole-brain, voxel-based methods. In the healthy controls, task performance was found to be associated with activations in the left dorsolateral prefrontal cortex and the dorsal anterior cingulate cortex, among other regions. De-activations were seen in the medial frontal cortex, the middle and posterior cingulate gyrus and cuneus, the parahippocampal gyrus and the hippocampus. The schizophrenic patients did not show reduced activation compared to the healthy controls. They did, however, show failure of de-activation in the medial frontal cortex. Our negative finding with respect to hypoactivation during performance of a task requiring inhibition of prepotent responses suggests that brain functional abnormality in schizophrenia may not affect all aspects of executive function/cognitive control. The finding of medial frontal cortex failure of de-activation adds to existing findings of default mode network dysfunction in the disorder.

Posterior fronto-medial atrophy reflects decreased loss aversion, but not executive impairment, in alcohol use disorder.

Decreased punishment sensitivity in alcohol use disorder (AUD) might reflect a reduction in the typical human tendency to overweigh negative choice outcomes compared with equivalent positive ones, that is, 'loss aversion.' While this hypothesis is supported by previous reports of reduced loss aversion in AUD, it is still unknown whether such decreased sensitivity to prospective losses represents a specific facet of altered decision-making or a secondary effect of executive/working-memory impairments. We addressed this issue by assessing whether lower loss aversion in 22 AUD patients compared with 19 healthy controls is explained by their differential executive or working-memory performance and by investigating its neural basis in terms of grey matter density and cortical thickness via voxel- and surface-based morphometry, respectively. A significant decrease of loss aversion in patients, unrelated to their impaired executive/working-memory performance, reflected the reduction of posterior fronto-medial grey matter density and right frontopolar cortical thickness. Rather than their executive deficits, patients' reduced loss aversion reflects the structural damage of the posterior fronto-medial cortex previously associated with solving conflicts at the response level, where earlier functional magnetic resonance imaging (fMRI) studies have shown a 'neural loss aversion' pattern of steeper deactivation for losses than activation for gains, and of the frontopolar cortex in charge of managing competing goals. These findings highlight possible directions for addressing AUD patients' high relapse rate, for example, cognitive-behavioural rehabilitative interventions enhancing the awareness of the adverse outcomes of addiction or neurostimulation protocols targeting the regions processing their salience.