Heiner Stuke

Effects of cognitive bias modification training on neural alcohol cue reactivity in alcohol dependence

OBJECTIVE In alcohol-dependent patients, alcohol cues evoke increased activation in mesolimbic brain areas, such as the nucleus accumbens and the amygdala. Moreover, patients show an alcohol approach bias, a tendency to more quickly approach than avoid alcohol cues. Cognitive bias modification training, which aims to retrain approach biases, has been shown to reduce alcohol craving and relapse rates. The authors investigated effects of this training on cue reactivity in alcohol-dependent patients. METHOD In a double-blind randomized design, 32 abstinent alcohol-dependent patients received either bias modification training or sham training. Both trainings consisted of six sessions of the joystick approach-avoidance task; the bias modification training entailed pushing away 90% of alcohol cues and 10% of soft drink cues, whereas this ratio was 50/50 in the sham training. Alcohol cue reactivity was measured with functional MRI before and after training. RESULTS Before training, alcohol cue-evoked activation was observed in the amygdala bilaterally, as well as in the right nucleus accumbens, although here it fell short of significance. Activation in the amygdala correlated with craving and arousal ratings of alcohol stimuli; correlations in the nucleus accumbens again fell short of significance. After training, the bias modification group showed greater reductions in cue-evoked activation in the amygdala bilaterally and in behavioral arousal ratings of alcohol pictures, compared with the sham training group. Decreases in right amygdala activity correlated with decreases in craving in the bias modification but not the sham training group. CONCLUSIONS These findings provide evidence that cognitive bias modification affects alcohol cue-induced mesolimbic brain activity. Reductions in neural reactivity may be a key underlying mechanism of the therapeutic effectiveness of this training.

A predictive coding account of bistable perception - a model-based fMRI study

In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model’s predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants’ perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work provides a theoretical framework that allows for the analysis of behavioural and neural data using a predictive coding perspective on bistable perception. In this, our approach posits a crucial role of prediction error signalling for the resolution of perceptual ambiguities.

Decreased gray matter volume in inferior frontal gyrus is related to stop-signal task performance in alcohol-dependent patients.

Impairment in inhibitory control has been proposed to contribute to habitual alcohol use, abuse and eventually dependence. Moreover, alcohol-dependent (AD) patients have shown a loss of gray matter volume (GMV) in the brain, specifically in prefrontal regions associated with executive functions, including response inhibition. To date, no study has evaluated whether this prefrontal GMV reduction is related to response inhibition in alcohol dependence. To address this issue, we acquired high-resolution T1-weighted magnetic resonance mages from recently detoxified AD patients (n = 22) and healthy controls (HC; n = 21). Differences in local GMV between groups were assessed by means of voxel-based morphometry (VBM). Moreover, within the AD group, mean local GMV reductions were extracted and correlated with behavioral performance on the stop-signal task. We found a significantly decrease in GMV in the left inferior frontal gyrus (IFG) in AD patients compared with HC subjects. Further, mean local GMV in this area correlated positively with reaction times on go trials during the stop-signal task in AD patients. Our findings suggest that GMV losses in the IFG in AD patients are related to faster go responses on the stop-signal task.

Cross-Sectional and Longitudinal Relationships between Depressive Symptoms and Brain Atrophy in MS Patients.

Introduction: Depressive symptoms are a frequent and distressing phenomenon in Multiple Sclerosis (MS) patients. Cross-sectional research links these symptoms to reduced brain gray matter volumes in parts of the prefrontal and temporal lobe as well as subcortical structures like the hippocampus, nucleus caudatus and globus pallidus. Nevertheless, prospective relationships between regional gray matter volume and the course of depressive symptoms are poorly understood. Methods: Forty-four patients with relapsing–remitting or secondary progressive MS participated in a prospective study with two assessments of depressive symptoms and high-resolution MRI with an inter-test-interval of 17 months. Relationships between baseline gray matter volume and baseline depressive symptoms, as well as prospective associations between the development of atrophy and depression were assessed using voxel-based morphometry (VBM). Results: Cross-sectional analyses revealed an association between depressive symptoms and gray matter loss in the left temporal lobe. Prospective analysis showed that gray matter losses in the right middle cingulate and middle frontal gyrus at baseline predicted increasing depressive symptoms during follow-up. Increase in depressive symptoms was related to a concomitant increase in atrophy in the left thalamus and right globus pallidus. Discussion: Our results fit well into the concept of a disturbed cortico–striatal–pallido–thalamic loop in depression. In this framework, progressive gray matter loss in limbic basal ganglia structures including globus pallidus and thalamus may lead to depression-typical deficits in hedonic motivation, whereas atrophy of the prefrontal cortex may contribute to maladaptive coping strategies, promoting an unfavorable development of depressive symptoms.

Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning

Theoretical accounts suggest that an alteration in the brain’s learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.

Comparing Three Cognitive Biases for Alcohol Cues in Alcohol Dependence

Aims There is accumulating evidence that automatic processes play a large role in alcohol dependence, which may be related to alcohol craving and consumption. The aim of this study is to investigate associations between cognitive biases in alcohol-dependent patients, and how these measures relate to drinking behavior. Methods Thirty alcohol-dependent patients and 15 healthy controls (matched for age, intelligence and education; all male) completed three cognitive bias tasks: the Implicit Association Test (IAT: alcohol-approach association), Approach Avoidance Task (AAT: alcohol approach bias) and Dot Probe Task (DPT: alcohol attentional bias). Task scores were compared between groups and correlated with each other, as well as with craving scores and drinking behavior. Results Patients with alcohol dependence showed stronger alcohol-approach associations on the IAT compared with controls, but there were no group differences for approach or attentional biases. Within the patient group, the alcohol approach bias (AAT) correlated positively with the attend-alcohol attentional bias (DPT), but negatively with alcohol-approach associations (IAT). IAT scores were positively associated with lifetime alcohol intake. Conclusions This study demonstrates for the first time that alcohol-dependent patients have stronger alcohol-approach association scores on the IAT as compared to controls, and that this bias is associated with drinking behavior. Despite the absence of group differences for the approach and attentional biases, the positive correlation between these biases in alcoholics is in line with incentive salience models of addiction that propose that attentional and approach tendencies have a common underlying mechanism, distinct from that underlying alcohol-approach associations measured by the IAT. Short Summary The study investigates associations between cognitive biases involving alcohol cues. Patients with alcohol dependence showed stronger alcohol-approach associations on an Implicit Association Test than controls, but there were no group differences for approach or attentional biases. Alcohol-approach and attentional bias correlated positively in the patient group.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although “high-level” predictions about the cue–target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic “low-level” predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment. SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that “high-level” predictions about the strength of dynamic cue–target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas “low-level” conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain.

Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment

With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1–42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer’s Disease—Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

Correction: Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning

[This corrects the article DOI: 10.1371/journal.pcbi.1005328.].