Biology subjects

Browning, M.

Publications and source records attributed to Browning, M..

18 recordsLinked to original sources

bioRxiv2026

Perturbation-based propagation of bacterial composition uncertainty improves robustness and reproducibility of microbiome differential abundance analysis.

Differential abundance (DA) analysis remains the analytical bottleneck of microbiome science; despite dozens of available methods, results remain poorly reproducible across tools, datasets, and study designs. Three intertwined challenges drive this failure: compositional data structure, stochastic count uncertainty, and complex experimental design; yet no existing method addresses all three simultaneously. Here, we introduce PARALLAX (Perturbation-based Assessment of Robustness Across Latent Logical Axes), a unified DA framework that resolves this gap by propagating compositional uncertainty through per-replicate linear mixed modeling across a structured scale perturbation grid, then summarizes inference through two stability-based metrics: Ppost (effect consistency across Monte Carlo replicates) and Rscale (robustness to scale perturbation). PARALLAX retains uncertainty through the complete inferential pipeline while natively supporting repeated measures and multifactorial designs. Under repeated subsampling, PARALLAX identified fully stable DA taxa in 14.4% of cases, versus 1.7% and 0% for comparators. We applied PARALLAX to two independent longitudinal shotgun metagenomic datasets from mechanistically distinct mouse models: Gulf War toxicant exposure and repetitive head trauma. In both, PARALLAX resolved robust, sex-specific, and time-dependent genus-level shifts while accommodating repeated measures and multifactorial design, tasks that challenged both comparator frameworks. PARALLAX is released as an open-source, AI-compatible R workflow, lowering the barrier to rigorous microbiome DA analysis across complex study designs.

bioinformatics↗

bioRxiv2026

Valence-specific representation of uncertainty in the anterior cingulate is impaired in those with affective symptoms

The ability to seek reward and avoid punishment is a fundamental survival instinct. In natural environments, however, the statistics of rewards and punishments can change independently of one another. In addition, individuals experiencing anxiety and depression may be selectively biased to process rewards and punishments differently. Here, we examine how humans adapt their behavior in such dynamic environments, using a task where cues are associated with independently changing probabilities of rewards (wins) and punishments (losses). We demonstrate that participants dynamically adjust their learning rates based on the relative volatility of each valence. Neuroimaging reveals that this behavioral flexibility is supported by valence-specific segregation of volatility signals within distinct subregions of the anterior cingulate cortex (ACC: perigenual and dorsal). Furthermore, individuals with higher levels of anxiety and depression exhibit a relative reduction in loss-volatility learning adaptation, accompanied by less distinct neural encoding of win and loss volatility in both ACC subregions. Together, our findings indicate that flexible adaptation to separate reward and punishment contingencies relies on distinct, valence-specific tracking of volatility within the prefrontal cortex. These findings suggest a potential computational and neuroanatomical framework for understanding maladaptive learning in affective disorders.

neuroscience↗

bioRxiv2025

Synaptic histamine shapes the neurocomputational dynamics of human learning

Histamine was the first canonical monoamine identified in the mammalian brain 1-6, yet arguably remains the least understood in its mechanistic contributions to human behaviour. Using a first-in-class causal probe (H3R inverse agonist pitolisant), we show how increased synaptic histamine shapes offline and online temporal-hippocampal dynamics, sustaining learning-related activity and polarising retrieval computations. At a broader level, elevated histamine adaptively shifts neurocomputational strategy under high cognitive load, while stabilising value updates during aversive reinforcement learning. These findings uncover a mechanistically grounded influence of this underexplored system on human neurocomputation, supporting its therapeutic potential in psychiatry.

neuroscience↗

bioRxiv2025

Differential effects of dopamine and serotonin on reward and punishment processes in humans: A systematic review and meta-analysis

ImportanceTo support treatment assignment, mechanistic biomarkers should be selectively sensitive to specific interventions. Here, we examine whether different components of reinforcement learning in humans satisfy this necessary precondition. We focus on pharmacological manipulations of dopamine and serotonin that form the backbone of first-line management of common mental illnesses such as depression and anxiety. ObjectiveTo perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans. Data SourcesOvid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946 and January 19, 2023 (repeated April 9, 2024, and October 15, 2024) investigating dopaminergic or serotonergic effects on reward/punishment processes in humans, according to PRISMA guidelines. Study SelectionStudies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward/punishment processing task in healthy humans were included. Data Extraction and SynthesisStandardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine/serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward/punishment processes and four main subcategories. Study quality (Cochrane Collaborations tool), moderators, heterogeneity, and publication bias were also assessed. Main Outcome(s) and Measure(s)Performance on reward/punishment processing tasks. ResultsIn total, 68 dopamine and 39 serotonin studies in healthy volunteers were included (Ndopamine=2291, Nplacebo=2284; Nserotonin=1491, Nplacebo=1523). Dopamine was associated with an increase in overall reward (SMD=0.18, 95%CI [0.09 0.28]) but not punishment function (SMD=-0.06, 95%CI [-0.26,0.13]). Serotonin was not meaningfully associated with overall punishment (SMD=0.22, 95%CI [-0.04,0.49]) or reward (SMD=0.02, 95%CI [-0.33,0.36]). Importantly, dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning/sensitivity (SMD=0.26, 95%CI [0.11,0.40]), reward discounting (SMD=-0.08, 95%CI [-0.14,-0.01]) and reward vigor (SMD=0.32, 95%CI [0.11,0.54]). By contrast, serotonin was associated with punishment learning/sensitivity (SMD=0.32, 95%CI [0.05,0.59]), reward discounting (SMD=-0.35, 95%CI [-0.67,-0.02]), and aversive Pavlovian processes (within-subject studies only; SMD=0.36, 95%CI [0.20,0.53]). Conclusions and RelevancePharmacological manipulations of both dopamine and serotonin have measurable associations with reinforcement learning in humans. The selective associations with different components suggests that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment. Key pointsO_ST_ABSQuestionC_ST_ABSDo pharmacological manipulations of dopamine and serotonin affect components of reinforcement learning in humans? FindingsUpregulating dopamine is associated with increased reward learning/sensitivity and reward response vigor, and decreased reward discounting. Upregulation of serotonin is associated with increased punishment learning/sensitivity and decreased reward discounting. MeaningPharmacological manipulations of dopamine and serotonin have dissociable associations with different components of reinforcement learning. This forms a necessary basis for the development of selective markers for treatment assignment.

neuroscience↗

bioRxiv2024

Noradrenaline causes a spread of association in the hippocampal cognitive map

The mammalian brain stores relationships between items and events in the world as cognitive maps in the hippocampal system. However, the neural mechanisms that control cognitive map formation remain unclear. Here, using a double-blind study design with a pharmacological intervention in humans, we show that the neuromodulator noradrenaline elicits a significant spread of association across the hippocampal cognitive map. This neural spread of association predicts overgeneralisation errors measured in behaviour, and is predicted by physiological markers for noradrenergic arousal, namely an increase in the pupil dilation response and reduced cortical GABAergic tone. Thus, noradrenaline sets the width of the smoothing kernel for plasticity across the cognitive map. Elevated noradrenaline can allow disparate memories to become linked, introducing potential for distortions in the cognitive map that deviate from direct experience. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=137 HEIGHT=200 SRC="FIGDIR/small/610602v3_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@85d4ceorg.highwire.dtl.DTLVardef@564073org.highwire.dtl.DTLVardef@a16111org.highwire.dtl.DTLVardef@ebe1fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Humans adapt rationally to approximate estimates of uncertainty

Efficient learning requires estimation of, and adaptation to, different forms of uncertainty. If uncertainty is caused by randomness in outcomes (noise), observed events should have less influence on beliefs, whereas if uncertainty is caused by a change in the process being estimated (volatility) the influence of events should increase. Previous work has demonstrated that humans respond appropriately to changes in volatility, but there is less evidence of a rational response to noise. Here, we test adaptation to variable levels of volatility and noise in human participants, using choice behaviour and pupillometry as a measure of the central arousal system. We find that participants adapt as expected to changes in volatility, but not to changes in noise. Using a Bayesian observer model, we demonstrate that participants are, in fact, adapting to estimated noise, but that their estimates are imprecise, leading them to misattribute it as volatility and thus to respond inappropriately.

neuroscience↗

bioRxiv2022

Trait anxiety is associated with hidden state inference during aversive reversal learning

Updating beliefs in changing environments can be driven by gradually adapting expectations or by relying on inferred hidden states (i.e., contexts), and changes therein. Previous work suggests that increased reliance on context could underly fear relapse phenomena that hinder clinical treatment of anxiety disorders. We test whether trait anxiety variations in a healthy population influence how much individuals rely on hidden-state inference. In a Pavlovian learning task, participants observed cues that predicted an upcoming electrical shock with repeatedly changing probability, and were asked to provide expectancy ratings on every trial. We show that trait anxiety is associated with steeper expectation switches after contingency reversals and reduced oddball learning. Furthermore, trait anxiety is related to better fit of a state inference compared to a gradual learning model when contingency changes are large. Our findings support previous work suggesting hidden-state inference as a mechanism behind anxiety-related to fear relapse phenomena.

neuroscience↗

bioRxiv2021

Stimulating human prefrontal cortex increases reward learning

Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it is proposed that depressed individuals believe that negative events are more informative than positive events, resulting in faster learning from negative outcomes (Pulcu & Browning, 2019). In this proof-of-concept study, we investigated whether learning rates for affective outcomes are malleable using transcranial direct current stimulation (tDCS). Healthy adults completed an established reinforcement learning task (Pulcu & 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) specifically increased learning rates for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: online stimulation increased learning rates for wins; offline stimulation decreased both win and loss learning rates. A replication study confirmed the key finding that online tDCS to DLPFC specifically increased learning rates for rewards relative to losses. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning relevant to mood disorders. Significance statementDisproportionate learning from negative relative to positive outcomes has been implicated in the development and maintenance of depression. The present work demonstrates that transcranial direct current stimulation (tDCS) to dorsolateral prefrontal cortex can specifically increase learning from positive events in healthy adults. Our results provide preliminary evidence that non-invasive brain stimulation can be used to shape reinforcement learning, indicating a potential novel cognitive neurostimulation intervention strategy for affective disorders.

neuroscience↗

bioRxiv2019

Pupillary correlates of computations underlying human responder behaviour in the Ultimatum Game

Negotiating with others about how finite resources should be distributed is an important aspect of human social life. However, little is known about mechanisms underlying human social-interactive decision-making. Here, we report results from a novel iterative Ultimatum Game (UG) task, in which the proposers facial emotions and offer amounts were sampled probabilistically based on the participants decisions, creating a gradually evolving social-interactive decision-making environment. Our model-free results confirm the prediction that both the proposers facial emotions and the offer amount influence human choice behaviour. These main effects demonstrate that biases in facial emotion recognition also contribute to violations of the Rational Actor model (i.e. all offers should be accepted). Model-based analyses extend these findings, indicating that participants decisions are guided by an aversion to inequality in the UG. We highlight that the proposers facial responses to participant decisions dynamically modulate how human decision-makers perceive self-other inequality, relaxing its otherwise negative influence on decision values. In iterative games, this cognitive model underlies how offers initially rejected can gradually become more acceptable under increasing affective load, and accurately predicts 86% of participant decisions. Activity of the central arousal systems, assessed by measuring pupil size, encode a key element of this model: proposers affective reactions in response to participant decisions. Taken together, our results demonstrate that, under affective load, participants aversion to inequality is a malleable cognitive process which is modulated by the activity of the pupil-linked central arousal systems.

neuroscience↗

bioRxiv2019

Neurocognitive mechanisms of d-cycloserine augmented single-session exposure therapy for anxiety

ObjectiveDrugs targeting the N-Methyl-D-aspartic acid (NMDA) system and the ability to learn new associations have been proposed as potential adjunct treatments to boost the success of exposure therapy for anxiety disorders. However, the effects of the NMDA partial agonist d-cycloserine on psychological treatment have been mixed. We investigated potential neurocognitive mechanisms underlying the clinical effects of d-cycloserine-augmented exposure, to inform the optimal combination of this and similar agents with psychological treatment. MethodsUnmedicated patients with panic disorder were randomised to single-dose d-cycloserine (250mg; N=17) or matching placebo (N=16) 2hrs before one session of exposure therapy. Neurocognitive markers were assessed one day after treatment, including reaction-time based threat bias for fearful faces and amygdala response to threat. Clinical symptom severity was measured using self-report and clinician-rated scales the day before and after treatment, and at 1- and 6-months follow-up. Analysis was by intention-to-treat. ResultsOne day after treatment, threat bias for fearful faces and amygdala threat response were attenuated in the drug compared to the placebo group. Lower amygdala magnitude predicted greater clinical improvement during follow-up across groups. D-cycloserine led to greater clinical recovery at 1-month follow-up (d-cycloserine 71% versus placebo 25%). DiscussionD-cycloserine-augmented single-session exposure therapy reduces amygdala threat response, and this effect predicts later clinical response. These findings highlight a neurocognitive mechanism by which d-cycloserine may exert its augmentative effects on psychological treatment and bring forward a marker that may help understand and facilitate future development of adjunct treatments with CBT for anxiety disorders. (D-cycloserine Augmented CBT for Panic Disorder; clinicaltrials.gov; NCT01680107)

clinical trials↗

bioRxiv2019

D-Cycloserine as Adjunct to Brief Computerised CBT for Spider Fear: Effects on Fear, Behaviour, and Cognitive Biases

In anxiety disorders, cognitive behavioural therapy (CBT) improves information-processing biases such as implicit fear evaluations and avoidance tendencies, which predicts treatment response, so they might constitute important treatment targets. This study investigated (i) whether information-processing biases changed following single-session computerised CBT for spider fear, and (ii) whether this effect could be augmented by administration of D-cycloserine (DCS). Spider-fearful individuals were randomized to receiving 250mg of DCS (n=21) or placebo (n=17) and spider fear was assessed using self-report, behavioural, and information-processing (Extrinsic Affective Simon Task & Approach Avoidance Task) measures. Linear mixed-effects analyses indicated improvements on self-report and behavioural spider fear following CBT, but not on cognitive bias measures. There was no evidence of an augmentation effect of DCS on any outcome. Cognitive biases at 1-day were not predictive of 1-month follow-up spider fear. These findings provide no evidence for information-processing biases relating to CBT response or augmentation with DCS.

neuroscience↗

bioRxiv2018

The effects of the angiotensin II receptor antagonist losartan on appetitive versus aversive learning

Exposure therapy is a first-line treatment for anxiety disorders but remains ineffective in a large proportion of patients. A proposed mechanism of exposure involves a form of inhibitory learning where the association between a stimulus and an aversive outcome is suppressed by a new association with an appetitive or neutral outcome. The blood pressure medication losartan augments fear extinction in rodents and might have similar synergistic effects on human exposure therapy, but the exact cognitive mechanisms underlying these effects remain unknown. In this study, we used a reinforcement learning paradigm with compound rewards and punishments to test the prediction that losartan augments learning from appetitive relative to aversive outcomes. Healthy volunteers (N=53) were randomly assigned to single-dose losartan (50mg) versus placebo. Participants then performed a reinforcement learning task which simultaneously probes appetitive and aversive learning. Participant choice behaviour was analysed using both a standard reinforcement learning model and by analysis of choice switching behaviour. Losartan significantly reduced learning rates from aversive events (losses) when participants were first exposed to the novel task environment, while preserving learning from positive outcomes. The same effect was seen in choice switching behaviour. Losartan enhances learning from positive relative to negative events. This effect may represent a computationally defined neurocognitive mechanism by which the drug could enhance the effect of exposure in clinical populations.

neuroscience↗

bioRxiv2018

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

BackgroundClinically approved antidepressants modulate the brains 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.\n\nMethodsWe 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.\n\nResultsWe found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50-87% 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.\n\nConclusionsWe 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.

neuroscience↗

bioRxiv2018

Emotional recognition training modifies neural response to emotional faces but does not improve mood in healthy volunteers with high levels of depressive symptoms

IMPORTANCEDepression is a debilitating and highly prevalent mental health disorder. There is a need for new, effective, and scalable treatments for depression, and cognitive bias modification (CBM) of negative emotional processing biases has been suggested as one possibility. Such treatments may form the basis of digital therapeutics, that could be administered remotely and at low cost, should they prove to be effective.\n\nOBJECTIVESStudy one was designed to determine neural correlates of a recently developed CBM technique for emotion recognition training; specifically, our aim was to compare the effects of training vs placebo on pre-specified regions of interest involved in emotion processing that are known to be sensitive to antidepressant treatment. Study two aimed to investigate efficacy of training on mood measures at 2 and 6-week follow-up and was powered to replicate and extend earlier findings.\n\nDESIGN, SETTING, AND PARTICIPANTSBoth studies were double blind RCTs, in which participants completed five sessions of emotion recognition training or sham training, in the laboratory, over a one-week period. In study one (N=37), following this training, participants completed a novel emotion recognition task whilst undergoing fMRI. In study two (N=190), measures of mood were assessed post training, and at 2-week and 6-week follow-up. Both studies recruited analogue samples of healthy volunteers with high levels of depressive symptoms (BDI-ii > 14).\n\nMAIN OUTCOMES AND MEASURESIn study one, our primary outcome was neural activation in the following pre-specified regions of interest: the bilateral amygdala, the mPFC, bilateral dlPFC, and the occipital cortex. In study two, our primary outcome was depressive symptoms over the last 2 weeks assessed using the BDI-ii at 6-week follow-up. Secondary outcomes included depressive symptoms measured using the HAM-D, and positive and negative affect assessed using the PANAS.\n\nRESULTSIn both studies, CBM resulted in a change in emotion recognition bias, which (in study two) persisted for 6 weeks after the end of the CBM phase. In study one, CBM resulted in increases neural activation to happy faces compared to sad faces, with this effect driven by an increase in neural activity for happy faces. We saw this increase in activation for this contrast at both the whole brain level and among our a priori ROIs, specifically the mPFC and bilateral amygdala. In study two, CBM did not lead to a reduction in depressive symptoms on the BDI-ii, or on related measures of mood, motivation and persistence, or depressive interpretation bias.\n\nCONCLUSIONS AND RELEVANCECBM of emotion recognition appears to have effects on neural activity that are similar in some respects to those induced by SSRI administration (study one), but we find no evidence that this has any effect on self-reported mood in an analogue sample of healthy volunteers with low mood (study two).

neuroscience↗

bioRxiv2018

Attentional Bias Modification Alters fMRI Response towards Negative Stimuli in Residual Depression

BackgroundModification of attentional biases (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for future treatment development. Task-related fMRI following ABM training has so far not been investigated in depression. The main aim of the RCT was to explore differences in brain activity after ABM training in response to emotional stimuli.\n\nMethodsA total of 134 previously depressed individuals were randomized into 14 days of ABM- or a placebo training followed by an fMRI emotion regulation task. Depression symptoms and subjective ratings of perceived negativity during fMRI was examined between the training groups. Brain activation was explored within predefined areas (SVC) and across the whole brain. Activation in areas associated with changes in attentional biases (AB) and degree of depression was explored.\n\nResultsThe ABM group showed reduced activation within the amygdala and within the anterior cingulate cortex (ACC) when passively viewing negative images compared to the placebo group. No group differences were found within predefined SVCs associated with emotion regulation strategies. Response within the temporal cortices was associated with degree of change in AB and with degree of depressive symptoms in ABM versus placebo.\n\nLimitationsThe findings should be replicated in other samples of depressed patients and in studies using designs that allow analyses of within-group variability from baseline to follow-up.\n\nConclusionsABM training has an effect on brain function within circuitry associated with emotional appraisal and the generation of affective states.\n\nClinicaltrials.gov identifier: NCT02931487

clinical trials↗

bioRxiv2018

Effects of Attentional Bias Modification on Residual Symptoms in depression. A Randomized Controlled Trial.

BackgroundFollowing treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested.\n\nAimsTo evaluate the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction.\n\nMethodA total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training.\n\nResultsABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli associated with greater symptom reduction.\n\nConclusionThe current study demonstrates that ABM produces early changes in both AB and blinded clinician-rated depressive symptoms. ABM may have practical potential in the treatment of residual depression.\n\nClinicalTrials.gov ID: NCT02658682

clinical trials↗

bioRxiv2017

Prefrontal cortex regulates amygdala response to threat in trait anxiety

BackgroundHighly co-morbid mood and anxiety disorders are associated with aberrant fronto-limbic signalling during emotional processing. Animal models suggest that hypoactive prefrontal cortex weakens top-down control of limbic structures, causing heightened limbic and behavioural reactivity to negative information. Here we tested for this causal mechanism in human trait anxiety. We reasoned that if dorsolateral prefrontal cortex controls amygdala response to affective information, then stimulation of that brain region should reduce the hyperactive amygdala threat responsivity seen in trait anxiety.\n\nMethodsUsing a within-subjects design, sixteen high-trait anxious females received active and sham transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex, in counterbalanced order, with sessions timed to be at least one month apart. Each session was followed immediately by a functional magnetic resonance imaging (fMRI) scan during which participants performed an attentional task with threat-related distractors.\n\nResultsAs predicted, compared to sham stimulation, active prefrontal cortex stimulation reduced amygdala threat reactivity and simultaneously increased activity in cortical regions associated with attentional control and improved task accuracy.\n\nConclusionsThese results demonstrate a causal role for impoverished frontal regulation of amygdaloid function in attentional capture by threat in trait anxiety. The finding that prefrontal stimulation reduces amygdala threat reactivity acutely indicates a neurocognitive mechanism that could contribute to tDCS treatment effects in affective disorders.

neuroscience↗

bioRxiv2017

Characterising and Engaging a Computationally Defined Treatment Target for Depression

Affective bias, the tendency to prioritise the processing of negative relative to positive events, is causally linked to clinical depression. However, why such biases develop or how they may best be ameliorated is not known. Using a computational framework, we investigated whether affective biases may reflect an individuals estimates of the information content of negative and positive events. During a reinforcement learning task, the information content of positive and negative outcomes was manipulated independently by varying the volatility of their occurrence. Human participants altered the learning rates used for the outcomes selectively, preferentially learning from the most informative. This behaviour was associated with activity of the central norepinephrine system, estimated using pupilometry, for loss outcomes. Humans maintain independent estimates of the information content of positive and negative outcomes which bias their processing of affective events. Normalising affective biases using computationally inspired interventions may represent a novel treatment approach for depression.

neuroscience↗