Assessing the Construct Validity of Aberrant Salience

We sought to validate the psychometric properties of a recently developed paradigm that aims to measure salience attribution processes proposed to contribute to positive psychotic symptoms, the Salience Attribution Test (SAT). The “aberrant salience” measure from the SAT showed good face validity in previous results, with elevated scores both in high-schizotypy individuals, and in patients with schizophrenia suffering from delusions. Exploring the construct validity of salience attribution variables derived from the SAT is important, since other factors, including latent inhibition/learned irrelevance (LIrr), attention, probabilistic reward learning, sensitivity to probability, general cognitive ability and working memory could influence these measures. Fifty healthy participants completed schizotypy scales, the SAT, a LIrr task, and a number of other cognitive tasks tapping into potentially confounding processes. Behavioural measures of interest from each task were entered into a principal components analysis, which yielded a five-factor structure accounting for ∼75% of the variance in behaviour. Implicit aberrant salience was found to load onto its own factor, which was associated with elevated “Introvertive Anhedonia” schizotypy, replicating our previous finding. LIrr loaded onto a separate factor, which also included implicit adaptive salience, but was not associated with schizotypy. Explicit adaptive and aberrant salience, along with a measure of probabilistic learning, loaded onto a further factor, though this also did not correlate with schizotypy. These results suggest that the measures of LIrr and implicit adaptive salience might be based on similar underlying processes, which are dissociable both from implicit aberrant salience and explicit measures of salience

fMRI in Translation: The Challenges Facing Real-World Applications

Functional neuroimaging has increased our understanding of human brain function tremendously and has become a standard tool in clinical and cognitive neuroscience research. We briefly review its methodological foundations and describe remaining challenges for translational research. The application of neuroimaging results to individual subjects, for example in predicting treatment response or determining the veracity of a statement, is limited by these challenges, in particular by the anatomical and statistical procedures commonly employed. We thus argue for sincere caution in the translation of functional neuroimaging to real-world applications

Antidepressant medications in dementia: evidence and potential mechanisms of treatment-resistance

Depression in dementia is common, disabling and causes significant distress to patients and carers. Despite widespread use of antidepressants for depression in dementia, there is no evidence of therapeutic efficacy, and their use is potentially harmful in this patient group. Depression in dementia has poor outcomes and effective treatments are urgently needed. Understanding why antidepressants are ineffective in depression in dementia could provide insight into their mechanism of action and aid identification of new therapeutic targets. In this review we discuss why depression in dementia may be a distinct entity, current theories of how antidepressants work and how these mechanisms of action may be affected by disease processes in dementia. We also consider why clinicians continue to prescribe antidepressants in dementia, and novel approaches to understand and identify effective treatments for patients living with depression and dementia

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety

In vivo multi-parameter mapping of the habenula using MRI

The habenula is a small, epithalamic brain structure situated between the mediodorsal thalamus and the third ventricle. It plays an important role in the reward circuitry of the brain and is implicated in psychiatric conditions, such as depression. The importance of the habenula for human cognition and mental health make it a key structure of interest for neuroimaging studies. However, few studies have characterised the physical properties of the human habenula using magnetic resonance imaging because its challenging visualisation in vivo, primarily due to its subcortical location and small size. To date, microstructural characterization of the habenula has focused on quantitative susceptibility mapping. In this work, we complement this previous characterisation with measures of longitudinal and effective transverse relaxation rates, proton density and magnetisation transfer saturation using a high-resolution quantitative multi-parametric mapping protocol at 3T, in a cohort of 26 healthy participants. The habenula had consistent boundaries across the various parameter maps and was most clearly visualised on the longitudinal relaxation rate maps. We have provided a quantitative multi-parametric characterisation that may be useful for future sequence optimisation to enhance visualisation of the habenula, and additionally provides reference values for future studies investigating pathological differences in habenula microstructure

Reliability of Fronto-Amygdala Coupling during Emotional Face Processing.

One of the most exciting translational prospects for brain imaging research is the potential use of functional magnetic resonance imaging (fMRI) 'biomarkers' to predict an individual's risk of developing a neuropsychiatric disorder or the likelihood of responding to a particular intervention. This proposal depends critically on reliable measurements at the level of the individual. Several previous studies have reported relatively poor reliability of amygdala activation during emotional face processing, a key putative fMRI 'biomarker'. However, the reliability of amygdala connectivity measures is much less well understood. Here, we assessed the reliability of task-modulated coupling between three seed regions (left and right amygdala and the subgenual anterior cingulate cortex) and the dorsomedial frontal/cingulate cortex (DMFC), measured using a psychophysiological interaction analysis in 29 healthy individuals scanned approximately two weeks apart. We performed two runs on each day of three different emotional face-processing tasks: emotion identification, emotion matching, and gender classification. We tested both between-day reliability and within-day (between-run) reliability. We found good-to-excellent within-subject reliability of amygdala-DMFC coupling, both between days (in two tasks), and within day (in one task). This suggests that disorder-relevant regional coupling may be sufficiently reliable to be used as a predictor of treatment response or clinical risk in future clinical studies

Attractor-like dynamics in belief updating in schizophrenia

Subjects with a diagnosis of schizophrenia (Scz) overweight unexpected evidence in probabilistic inference: such evidence becomes 'aberrantly salient'. A neurobiological explanation for this effect is that diminished synaptic gain (e.g. hypofunction of cortical N-methyl-D-aspartate receptors) in Scz destabilizes quasi-stable neuronal network states (or 'attractors'). This attractor instability account predicts that i) Scz would overweight unexpected evidence but underweight consistent evidence, ii) belief updating would be more vulnerable to stochastic fluctuations in neural activity, and iii) these effects would correlate.Hierarchical Bayesian belief updating models were tested in two independent datasets (n=80 and n=167, male and female) comprising human subjects with schizophrenia, and both clinical and non-clinical controls (some tested when unwell and on recovery) performing the 'probability estimates' version of the beads task (a probabilistic inference task). Models with a standard learning rate, or including a parameter increasing updating to 'disconfirmatory evidence', or a parameter encoding belief instability were formally compared.The 'belief instability' model (based on the principles of attractor dynamics) had most evidence in all groups in both datasets. Two of four parameters differed between Scz and non-clinical controls in each dataset: belief instability and response stochasticity. These parameters correlated in both datasets. Furthermore, the clinical controls showed similar parameter distributions to Scz when unwell, but were no different to controls once recovered.These findings are consistent with the hypothesis that attractor network instability contributes to belief updating abnormalities in Scz, and suggest that similar changes may exist during acute illness in other psychiatric conditions.SIGNIFICANCE STATEMENTSubjects with a diagnosis of schizophrenia (Scz) make large adjustments to their beliefs following unexpected evidence, but also smaller adjustments than controls following consistent evidence. This has previously been construed as a bias towards 'disconfirmatory' information, but a more mechanistic explanation may be that in Scz, neural firing patterns ('attractor states') are less stable and hence easily altered in response to both new evidence and stochastic neural firing. We model belief updating in Scz and controls in two independent datasets using a hierarchical Bayesian model, and show that all subjects are best fit by a model containing a belief instability parameter. Both this and a response stochasticity parameter are consistently altered in Scz, as the unstable attractor hypothesis predicts

Longitudinal decline in striatal dopamine transporter binding in Parkinson’s disease: associations with apathy and anhedonia

Background: Motivational symptoms such as apathy and anhedonia are common in Parkinson’s disease (PD), respond poorly to treatment, and are hypothesised to share underlying neural mechanisms. Striatal dopaminergic dysfunction is considered central to motivational symptoms in PD but the association has never been examined longitudinally. We investigated whether progression of dopaminergic dysfunction was associated with emergent apathy and anhedonia symptoms in PD. Methods: Longitudinal cohort study of 412 newly diagnosed patients with PD followed over 5 years as part of the Parkinson’s Progression Markers Initiative cohort. Apathy and anhedonia were measured using a composite score derived from relevant items of the 15-item Geriatric Depression Scale (GDS-15) and part I of the MDS-Unified Parkinson’s Disease Rating Scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging. Results: Linear mixed-effects modelling across all contemporaneous data points identified a significant negative relationship between striatal DAT specific binding ratio (SBR) and apathy/anhedonia symptoms, which emerged as PD progressed (interaction:β=−0.09, 95% CI (−0.15 to 0.03), p=0.002). Appearance and subsequent worsening of apathy/anhedonia symptoms began on average 2 years after diagnosis and below a threshold striatal DAT SBR level. The interaction between striatal DAT SBR and time was specific to apathy/anhedonia symptoms, with no evidence of a similar interaction for general depressive symptoms from the GDS-15 (excluding apathy/anhedonia items) (β=−0.06, 95% CI (−0.13 to 0.01)) or motor symptoms (β=0.20, 95% CI (−0.25 to 0.65)). Conclusions: Our findings support a central role for dopaminergic dysfunction in motivational symptoms in PD. Striatal DAT imaging may be a useful indicator of apathy/anhedonia risk that could inform intervention strategies