Author name cluster

Michael Browning

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

YNIMG Journal 2023 Journal Article

Stimulating human prefrontal cortex increases reward learning

Margot Juliëtte Overman
Verena Sarrazin
Michael Browning
Jacinta O'Shea

Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it has been proposed that depressed individuals believe that negative events are more informative than positive events, resulting in higher learning rates from negative outcomes (Pulcu and Browning, 2019). In this proof-of-concept study, we investigated whether transcranial direct current stimulation (tDCS) applied to dorsolateral prefrontal cortex, as commonly used in depression treatment trials, might change learning rates for affective outcomes. Healthy adults completed an established reinforcement learning task (Pulcu and Browning, 2017) in which the information content of reward and loss outcomes was manipulated by varying the volatility of stimulus-outcome associations. Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) increased learning rates specifically for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: tDCS applied during task performance increased learning rates for wins; tDCS applied before task performance decreased both win and loss learning rates. A replication study confirmed the key finding that tDCS to DLPFC during task performance increased learning rates specifically for rewards. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning that are relevant to mood disorders.

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YNICL Journal 2018 Journal Article

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants

Daniel S. Barron
Mehraveh Salehi
Michael Browning
Catherine J. Harmer
R. Todd Constable
Eugene Duff

Background: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n = 306) exploring the effect of antidepressant administration on emotional face processing. Methods: We created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies. Results: We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50 to 87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another. Conclusions: We found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development.

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YNICL Journal 2018 Journal Article

Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias

Janine D. Bijsterbosch
Tahereh L. Ansari
Stephen Smith
Oliver Gauld
Ondrej Zika
Sirius Boessenkool
Michael Browning
Andrea Reinecke

Patients with Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) show between-group comorbidity and symptom overlap, and within-group heterogeneity. Resting state functional connectivity might provide an alternate, biologically informed means by which to stratify patients with GAD or MDD. Resting state functional magnetic resonance imaging data were acquired from 23 adults with GAD, 21 adults with MDD, and 27 healthy adult control participants. We investigated whether within- or between-network connectivity indices from five resting state networks predicted scores on continuous measures of depression and anxiety. Successful predictors were used to stratify participants into two new groups. We examined whether this stratification predicted attentional bias towards threat and whether this varied between patients and controls. Depression scores were linked to elevated connectivity within a limbic network including the amygdala, hippocampus, VMPFC and subgenual ACC. Patients with GAD or MDD with high limbic connectivity showed poorer performance on an attention-to-threat task than patients with low limbic connectivity. No parallel effect was observed for control participants, resulting in an interaction of clinical status by resting state group. Our findings provide initial evidence for the external validity of stratification of MDD and GAD patients by functional connectivity markers. This stratification cuts across diagnostic boundaries and might valuably inform future intervention studies. Our findings also highlight that biomarkers of interest can have different cognitive correlates in individuals with versus without clinically significant symptomatology. This might reflect protective influences leading to resilience in some individuals but not others.

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YNIMG Journal 2012 Journal Article

Expectancy and surprise predict neural and behavioral measures of attention to threatening stimuli

Michael Browning
Catherine J. Harmer

Attention is preferentially deployed toward those stimuli which are threatening and those which are surprising. The current paper examines the intersection of these phenomena; how do expectations about the threatening nature of stimuli influence the deployment of attention? The predictions tested were that individuals would direct attention toward stimuli which were expected to be threatening (regardless of whether they were or not) and toward stimuli which were surprising. As anxiety has been associated with deficient control of attention to threat, it was additionally predicted that high levels of trait anxiety would be associated with deficits in the use of threat-expectation to guide attention. During fMRI scanning, 29 healthy volunteers completed a simple task in which threat-expectation was manipulated by altering the frequency with which fearful or neutral faces were presented. Individual estimates of threat-expectation and surprise were created using a Bayesian computational model. The degree to which the model derived estimates of threat-expectation and surprise were able to explain both a behavioral measure of attention to the faces and activity in the visual cortex and anterior attentional control areas was then tested. As predicted, increased threat-expectation and surprise were associated with increases in both the behavioral and neuroimaging measures of attention to the faces. Additionally, regions of the orbitofrontal cortex and left amygdala were found to covary with threat-expectation whereas anterior cingulate and lateral prefrontal cortices covaried with surprise. Individuals with higher levels of trait anxiety were less able to modify neuroimaging measures of attention in response to threat-expectation. These results suggest that continuously calculated estimates of the probability of threat may plausibly be used to influence the deployment of visual attention and that use of this information is perturbed in anxious individuals.

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