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Major depressive disorder (MDD) and psychostimulant use disorder (PUD) are common, disabling psychopathologies that pose a major public health burden. They share a common behavioral phenotype: deficits in inhibitory control (IC). However, whether this is underpinned by shared neurobiology remains unclear. In this meta-analytic study, we aimed to define and compare brain functional alterations during IC tasks in MDD and PUD.
Methods
We conducted a systematic literature search on IC task-based functional magnetic resonance imaging studies in MDD and PUD (cocaine or methamphetamine use disorder) in PubMed, Web of Science, and Scopus. We performed a quantitative meta-analysis using seed-based d mapping to define common and distinct neurofunctional abnormalities.
Results
We identified 14 studies comparing IC-related brain activation in a total of 340 MDD patients with 303 healthy controls (HCs), and 11 studies comparing 258 PUD patients with 273 HCs. MDD showed disorder-differentiating hypoactivation during IC tasks in the median cingulate/paracingulate gyri relative to PUD and HC, whereas PUD showed disorder-differentiating hypoactivation relative to MDD and HC in the bilateral inferior parietal lobule. In conjunction analysis, hypoactivation in the right inferior/middle frontal gyrus was common to both MDD and PUD.
Conclusions
The transdiagnostic neurofunctional alterations in prefrontal cognitive control regions may underlie IC deficits shared by MDD and PUD, whereas disorder-differentiating activation abnormalities in midcingulate and parietal regions may account for their distinct features associated with disturbed goal-directed behavior.
Depression is characterized by disturbed emotion processing, with aberrant neural and physiological responses to emotional stimuli. Here, we applied an emotion anticipation and processing paradigm to investigate brain neural and electrodermal reactivities in patients with depression compared with healthy controls.
Methods
The study included 42 patients (27 females) and 44 healthy controls (21 females). Subjects underwent functional magnetic resonance imaging with simultaneous measurement of electrodermal activity. During scanning, red or green color cues were presented, followed by pictures of negative or positive valence, respectively. Behavioral valence and arousal ratings of the picture stimuli were conducted after scanning. Anhedonia was assessed through a semi-structured interview in both subject groups.
Results
Patients perceived positive pictures as less positive than controls did. Positive anticipation (i.e., green color cues) elicited stronger activations in the anterior cingulate cortex and the right insula in patients than in healthy controls, indicating salience network disturbances. An exploratory analysis of all regions in the Automated Anatomical Labeling Atlas 2 found significant differences in activity to positive anticipation between groups in several brain regions involved in cognition and emotion processing. Positive and negative anticipation elicited stronger electrodermal responses in healthy controls. However, electrodermal reactivity to negative pictures was higher in patients than in controls.
Conclusions
Ongoing depression affects emotion anticipation and processing at the behavioral, neural, and physiological levels. These findings contribute to increased understanding of the disorder.
Cognitive–behavioural therapy (CBT) is a first-line treatment for depressive disorders, but research on its neurobiological mechanisms is limited. Given the heterogeneity in CBT response, investigating the neurobiological effects of CBT may improve response prediction and outcomes.
Aims
To examine brain functional changes during negative emotion processing following naturalistic CBT.
Method
In this case-control study, 59 patients with depressive disorders were investigated before and after 20 CBT sessions using a negative-emotion-processing paradigm during functional magnetic resonance imaging, clinical interviews and depressive symptom questionnaires. Healthy controls (n = 60) were also assessed twice within an equivalent time interval. Patients were classified into subgroups based on changes in diagnosis according to DSM-IV criteria (n = 40 responders, n = 19 non-responders). Brain activity changes were examined using group × time analysis of variance for limbic areas, and at the whole-brain level.
Results
Analyses yielded a significant group × time interaction in the hippocampus (P family-wise error [PFWE] = 0.022, ηP2 = 0.101), and a significant main effect of time in the dorsal anterior cingulate cortex (PFWE = 0.043, ηP² = 0.098), resulting from activity decreases following CBT (PFWE ≤ 0.024, ηP² ≤ 0.233), with no changes in healthy controls. Hippocampal activity decreases were driven by responders (PFWE ≤ 0.020, ηP² ≤ 0.260) and correlated with symptom improvement (r = 0.293, P = 0.024). Responders exhibited higher pre-treatment hippocampal activity (PFWE = 0.017, ηP² = 0.189).
Conclusions
Following CBT, reduced activity in emotion-processing regions was observed in patients with depressive disorders, with hippocampal activity decreases linked to treatment response. This suggests successful CBT could correct biased emotion processing, potentially by altering activity in key areas of emotion processing.Hippocampal activity may function as a predictive marker of CBT response.
Offspring of parents with bipolar disorder (BD offspring) face elevated risks for emotional dysregulation and cognitive deficits, particularly in working memory. This study investigates working memory deficits and their neural correlates in BD offspring.
Methods
We assessed 41 BD offspring and 25 age-matched healthy controls (HCs) using a spatial N-back task and task-related functional magnetic resonance imaging (fMRI).
Results
Compared to HCs, BD offspring exhibit reduced accuracy and lower signal-detection sensitivity (d′) on the 1-back task. fMRI reveals hyperactivation in the right intracalcarine cortex/lingual gyrus (ICC/LG) in BD offspring, particularly during the 1-back condition. Psychophysiological interaction (PPI) analyses show reduced connectivity between the right ICC/LG and the left postcentral gyrus in BD offspring as task load increases from 0-back to 1-back. This connectivity positively correlates with 1-back task performance in HCs but not in BD offspring. Additionally, using bilateral dorsolateral prefrontal cortex (DLPFC) as regions of interest, PPI analyses show diminished condition-dependent connectivity between the left DLPFC and the left superior frontal gyrus/paracingulate cortex, and between the right DLPFC and the left postcentral gyrus/precentral gyrus in BD offspring as the task load increases.
Conclusions
These findings suggest that BD offspring exhibit working memory deficits and impaired neural connectivity involving both sensory processing and higher-order cognitive systems. Such deficits may emerge at a genetically predisposed stage of bipolar disorder, underscoring the significance of early identification and intervention strategies.
Knowledge is growing on the essential role of neural circuits involved in aberrant cognitive control and reward sensitivity for the onset and maintenance of binge eating.
Aims
To investigate how the brain's reward (bottom-up) and inhibition control (top-down) systems potentially and dynamically interact to contribute to subclinical binge eating.
Method
Functional magnetic resonance imaging data were acquired from 30 binge eaters and 29 controls while participants performed a food reward Go/NoGo task. Dynamic causal modelling with the parametric empirical Bayes framework, a novel brain connectivity technique, was used to examine between-group differences in the directional influence between reward and executive control regions. We explored the proximal risk factors for binge eating and its neural basis, and assessed the predictive ability of neural indices on future disordered eating and body weight.
Results
The binge eating group relative to controls displayed fewer reward-inhibition undirectional and directional synchronisations (i.e. medial orbitofrontal cortex [mOFC]–superior parietal gyrus [SPG] connectivity, mOFC → SPG excitatory connectivity) during food reward_nogo condition. Trait impulsivity is a key proximal factor that could weaken the mOFC–SPG connectivity and exacerbate binge eating. Crucially, this core mOFC–SPG connectivity successfully predicted binge eating frequency 6 months later.
Conclusions
These findings point to a particularly important role of the bottom-up interactions between cortical reward and frontoparietal control circuits in subclinical binge eating, which offers novel insights into the neural hierarchical mechanisms underlying problematic eating, and may have implications for the early identification of individuals suffering from strong binge eating-associated symptomatology in the general population.
The Procrustes-based perturbation model (Goodall in J R Stat Soc Ser B Methodol 53(2):285–321, 1991) allows minimization of the Frobenius distance between matrices by similarity transformation. However, it suffers from non-identifiability, critical interpretation of the transformed matrices, and inapplicability in high-dimensional data. We provide an extension of the perturbation model focused on the high-dimensional data framework, called the ProMises (Procrustes von Mises–Fisher) model. The ill-posed and interpretability problems are solved by imposing a proper prior distribution for the orthogonal matrix parameter (i.e., the von Mises–Fisher distribution) which is a conjugate prior, resulting in a fast estimation process. Furthermore, we present the Efficient ProMises model for the high-dimensional framework, useful in neuroimaging, where the problem has much more than three dimensions. We found a great improvement in functional magnetic resonance imaging connectivity analysis because the ProMises model permits incorporation of topological brain information in the alignment’s estimation process.
Brain activation and connectivity analyses in task-based functional magnetic resonance imaging (fMRI) experiments with multiple subjects are currently at the forefront of data-driven neuroscience. In such experiments, interest often lies in understanding activation of brain voxels due to external stimuli and strong association or connectivity between the measurements on a set of pre-specified groups of brain voxels, also known as regions of interest (ROI). This article proposes a joint Bayesian additive mixed modeling framework that simultaneously assesses brain activation and connectivity patterns from multiple subjects. In particular, fMRI measurements from each individual obtained in the form of a multi-dimensional array/tensor at each time are regressed on functions of the stimuli. We impose a low-rank parallel factorization decomposition on the tensor regression coefficients corresponding to the stimuli to achieve parsimony. Multiway stick-breaking shrinkage priors are employed to infer activation patterns and associated uncertainties in each voxel. Further, the model introduces region-specific random effects which are jointly modeled with a Bayesian Gaussian graphical prior to account for the connectivity among pairs of ROIs. Empirical investigations under various simulation studies demonstrate the effectiveness of the method as a tool to simultaneously assess brain activation and connectivity. The method is then applied to a multi-subject fMRI dataset from a balloon-analog risk-taking experiment, showing the effectiveness of the model in providing interpretable joint inference on voxel-level activations and inter-regional connectivity associated with how the brain processes risk. The proposed method is also validated through simulation studies and comparisons to other methods used within the neuroscience community.
We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.
Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.
Methods
We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.
Results
Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.
Conclusions
Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.
Having social support improves one's health outcomes and self-esteem, and buffers the negative impact of stressors. Previous studies have explored the association between social support and brain activity, but evidence from task-dependent functional connectivity is still limited.
Aims
We aimed to explore how gradually decreasing levels of social support influence task-dependent functional connectivity across several major neural networks.
Method
We designed a social support task and recruited 72 young adults from real-life social groups. Of the four members in each group, one healthy participant (18 participants in total) completed the functional magnetic resonance imaging (fMRI) scan. The fMRI task included three phases with varying levels of social support: high-support phase, fair phase and low-support phase. Functional connectivity changes according to three phases were examined by generalised psychophysiological interaction analysis.
Results
The results of the analysis demonstrated that participants losing expected support showed increased connectivity among salience network, default mood network and frontoparietal network nodes during the fair phase compared with the high-support phase. During the low-support phase, participants showed increased connectivity among only salience network nodes compared with the high-support phase.
Conclusions
The results indicate that the loss of support was perceived as a threat signal and induced widespread increased functional connectivity within brain networks. The observation of significant functional connectivity changes between fair and high-support phases suggests that even a small loss of social support from close ones leads to major changes in brain function.
Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients.
Methods
Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls.
Results
Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks.
Conclusions
Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.
The modulation of brain circuits of emotion is a promising pathway to treat borderline personality disorder (BPD). Precise and scalable approaches have yet to be established. Two studies investigating the amygdala-related electrical fingerprint (Amyg-EFP) in BPD are presented: one study addressing the deep-brain correlates of Amyg-EFP, and a second study investigating neurofeedback (NF) as a means to improve brain self-regulation.
Methods
Study 1 combined electroencephalography (EEG) and simultaneous functional magnetic resonance imaging to investigate the replicability of Amyg-EFP-related brain activation found in the reference dataset (N = 24 healthy subjects, 8 female; re-analysis of published data) in the replication dataset (N = 16 female individuals with BPD). In the replication dataset, we additionally explored how the Amyg-EFP would map to neural circuits defined by the research domain criteria. Study 2 investigated a 10-session Amyg-EFP NF training in parallel to a 12-weeks residential dialectical behavior therapy (DBT) program. Fifteen patients with BPD completed the training, N = 15 matched patients served as DBT-only controls.
Results
Study 1 replicated previous findings and showed significant amygdala blood oxygenation level dependent activation in a whole-brain regression analysis with the Amyg-EFP. Neurocircuitry activation (negative affect, salience, and cognitive control) was correlated with the Amyg-EFP signal. Study 2 showed Amyg-EFP modulation with NF training, but patients received reversed feedback for technical reasons, which limited interpretation of results.
Conclusions
Recorded via scalp EEG, the Amyg-EFP picks up brain activation of high relevance for emotion. Administering Amyg-EFP NF in addition to standardized BPD treatment was shown to be feasible. Clinical utility remains to be investigated.
Reward processing dysfunctions are considered a candidate mechanism underlying anhedonia and apathy in depression. Neuroimaging studies have documented that neurofunctional alterations in mesocorticolimbic circuits may neurally mediate these dysfunctions. However, common and distinct neurofunctional alterations during motivational and hedonic evaluation of monetary and natural rewards in depression have not been systematically examined. Here, we capitalized on pre-registered neuroimaging meta-analyses to (1) establish general reward-related neural alterations in depression, (2) determine common and distinct alterations during the receipt and anticipation of monetary v. natural rewards, and, (3) characterize the differences on the behavioral, network, and molecular level. The pre-registered meta-analysis (https://osf.io/ay3r9) included 633 depressed patients and 644 healthy controls and revealed generally decreased subgenual anterior cingulate cortex and striatal reactivity toward rewards in depression. Subsequent comparative analyses indicated that monetary rewards led to decreased hedonic reactivity in the right ventral caudate while natural rewards led to decreased reactivity in the bilateral putamen in depressed individuals. These regions exhibited distinguishable profiles on the behavioral, network, and molecular level. Further analyses demonstrated that the right thalamus and left putamen showed decreased activation during the anticipation of monetary reward. The present results indicate that distinguishable neurofunctional alterations may neurally mediate reward-processing alterations in depression, in particular, with respect to monetary and natural rewards. Given that natural rewards prevail in everyday life, our findings suggest that reward-type specific interventions are warranted and challenge the generalizability of experimental tasks employing monetary incentives to capture reward dysregulations in everyday life.
Excessive negative self-referential processing plays an important role in the development and maintenance of major depressive disorder (MDD). Current measures of self-reflection are limited to self-report questionnaires and invoking imagined states, which may not be suitable for all populations.
Aims
The current study aimed to pilot a new measure of self-reflection, the Fake IQ Test (FIQT).
Method
Participants with MDD and unaffected controls completed a behavioural (experiment 1, n = 50) and functional magnetic resonance imaging version (experiment 2, n = 35) of the FIQT.
Results
Behaviourally, those with MDD showed elevated negative self-comparison with others, higher self-dissatisfaction and lower perceived success on the task, compared with controls; however, FIQT scores were not related to existing self-report measures of self-reflection. In the functional magnetic resonance imaging version, greater activation in self-reflection versus control conditions was found bilaterally in the inferior frontal cortex, insula, dorsolateral prefrontal cortex, motor cortex and dorsal anterior cingulate cortex. No differences in neural activation were found between participants with MDD and controls, nor were there any associations between neural activity, FIQT scores or self-report measures of self-reflection.
Conclusions
Our results suggest the FIQT is sensitive to affective psychopathology, but a lack of association with other measures of self-reflection may indicate that the task is measuring a different construct. Alternatively, the FIQT may measure aspects of self-reflection inaccessible to current questionnaires. Future work should explore relationships with alternative measures of self-reflection likely to be involved in perception of task performance, such as perfectionism.
The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM).
Methods
Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD.
Results
CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002).
Conclusions
This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
Identifying the optimal treatment for individuals with major depressive disorder (MDD) is often a long and complicated process. Functional magnetic resonance imaging (fMRI) studies have been used to help predict and explain differences in treatment response among individuals with MDD.
Objectives
We conducted a comprehensive meta-analysis of treatment prediction studies utilizing fMRI in patients with MDD to provide evidence that neural activity can be used to predict response to antidepressant treatment.
Methods
A multi-level kernel density analysis was applied to these primary fMRI studies, in which we analyzed brain activation patterns of depressed patients (N= 364) before receiving antidepressant treatment.
Results
The results of this analysis demonstrated that hyperactivity in six brain regions significantly predicted treatment response in patients with MDD: the right anterior cingulate, right cuneus, left fusiform gyrus, left middle frontal gyrus, right cingulate gyrus, and left superior frontal gyrus.
Conclusions
This study provides evidence that neural activity, as measured by standard fMRI paradigms, can be used to successfully predict response to antidepressant treatment. This may be used in the future clinically to improve decision-making processes and treatment outcomes for patients.
Glutamatergic dysfunction has been implicated in sensory integration deficits in schizophrenia, yet how glutamatergic function contributes to behavioural impairments and neural activities of sensory integration remains unknown.
Methods
Fifty schizophrenia patients and 43 healthy controls completed behavioural assessments for sensory integration and underwent magnetic resonance spectroscopy (MRS) for measuring the anterior cingulate cortex (ACC) glutamate levels. The correlation between glutamate levels and behavioural sensory integration deficits was examined in each group. A subsample of 20 pairs of patients and controls further completed an audiovisual sensory integration functional magnetic resonance imaging (fMRI) task. Blood Oxygenation Level Dependent (BOLD) activation and task-dependent functional connectivity (FC) were assessed based on fMRI data. Full factorial analyses were performed to examine the Group-by-Glutamate Level interaction effects on fMRI measurements (group differences in correlation between glutamate levels and fMRI measurements) and the correlation between glutamate levels and fMRI measurements within each group.
Results
We found that schizophrenia patients exhibited impaired sensory integration which was positively correlated with ACC glutamate levels. Multimodal analyses showed significantly Group-by-Glutamate Level interaction effects on BOLD activation as well as task-dependent FC in a ‘cortico-subcortical-cortical’ network (including medial frontal gyrus, precuneus, ACC, middle cingulate gyrus, thalamus and caudate) with positive correlations in patients and negative in controls.
Conclusions
Our findings indicate that ACC glutamate influences neural activities in a large-scale network during sensory integration, but the effects have opposite directionality between schizophrenia patients and healthy people. This implicates the crucial role of glutamatergic system in sensory integration processing in schizophrenia.
Aberrant anticipation of motivational salient events and processing of outcome evaluation in striatal and prefrontal regions have been suggested to underlie psychosis. Altered glutamate levels have likewise been linked to schizophrenia. Glutamatergic abnormalities may affect the processing of motivational salience and outcome evaluation. It remains unresolved, whether glutamatergic dysfunction is associated with the coding of motivational salience and outcome evaluation in antipsychotic-naïve patients with first-episode psychosis.
Methods
Fifty-one antipsychotic-naïve patients with first-episode psychosis (22 ± 5.2 years, female/male: 31/20) and 52 healthy controls (HC) matched on age, sex, and parental education underwent functional magnetic resonance imaging and magnetic resonance spectroscopy (3T) in one session. Brain responses to motivational salience and negative outcome evaluation (NOE) were examined using a monetary incentive delay task. Glutamate levels were estimated in the left thalamus and anterior cingulate cortex using LCModel.
Results
Patients displayed a positive signal change to NOE in the caudate (p = 0.001) and dorsolateral prefrontal cortex (DLPFC; p = 0.003) compared to HC. No group difference was observed in motivational salience or in levels of glutamate. There was a different association between NOE signal in the caudate and DLPFC and thalamic glutamate levels in patients and HC due to a negative correlation in patients (caudate: p = 0.004, DLPFC: p = 0.005) that was not seen in HC.
Conclusions
Our findings confirm prior findings of abnormal outcome evaluation as a part of the pathophysiology of schizophrenia. The results also suggest a possible link between thalamic glutamate and NOE signaling in patients with first-episode psychosis.
Numerous studies have demonstrated attentional control difficulties and high avoidance coping in patients with anorexia nervosa. Attention is a critical coping resource because it enables individuals to demonstrate self-control and complete goal-directed behaviours.
Aims
We aimed to examine whether attentional control difficulty is related to high avoidance coping, and investigate the neural underpinnings of attentional control difficulties in individuals with anorexia nervosa.
Method
Twenty-three patients with anorexia nervosa and 17 healthy controls completed questionnaires that assessed attention and coping, and underwent functional magnetic resonance imaging while performing a go/no-go task.
Results
Patients with anorexia nervosa showed weaker attentional control, higher omission error rates and higher avoidance coping compared with healthy controls. Attentional control difficulty was associated with higher avoidance coping in both groups. Functional magnetic resonance imaging analysis showed less deactivation in regions representing internal mental processing, such as the praecuneus, cuneus and left lingual gyrus, during the no-go condition. Moreover, weakened deactivation of the left lingual gyrus was associated with higher commission error rate in the anorexia nervosa group.
Conclusions
Our results suggest that patients with anorexia nervosa may have difficulty in maintaining attention to external ongoing events because of disturbance from internal self-related thought, and support the notion that attentional control difficulties underlie the frequent use of avoidance coping in anorexia nervosa.
Impulsivity is a central symptom of borderline personality disorder (BPD) and its neural basis may be instantiated in a frontoparietal network involved in response inhibition. However, research has yet to determine whether neural activation differences in BPD associated with response inhibition are attributed to attentional saliency, which is subserved by a partially overlapping network of brain regions.
Methods
Patients with BPD (n = 45) and 29 healthy controls (HCs; n = 29) underwent functional magnetic resonance imaging while completing a novel go/no-go task with infrequent odd-ball trials to control for attentional saliency. Contrasts reflecting a combination of response inhibition and attentional saliency (no-go > go), saliency processing alone (oddball > go), and response inhibition controlling for attentional saliency (no-go > oddball) were compared between BPD and HC.
Results
Compared to HC, BPD showed less activation in the combined no-go > go contrast in the right posterior inferior and middle-frontal gyri, and less activation for oddball > go in left-hemispheric inferior frontal junction, frontal pole, superior parietal lobe, and supramarginal gyri. Crucially, BPD and HC showed no activation differences for the no-go > oddball contrast. In BPD, higher vlPFC activation for no-go > go was correlated with greater self-rated BPD symptoms, whereas lower vlPFC activation for oddball > go was associated with greater self-rated attentional impulsivity.
Conclusions
Patients with BPD show frontoparietal disruptions related to the combination of response inhibition and attentional saliency or saliency alone, but no specific response inhibition neural activation difference when attentional saliency is controlled. The findings suggest a neural dysfunction in BPD underlying attention to salient or infrequent stimuli, which is supported by a negative correlation with self-rated impulsiveness.