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Depression has been associated with abnormalities in neural underpinnings of Reward Learning (RL). However, inconsistencies have emerged, possibly owing to medication effects. Additionally, it remains unclear how neural RL signals relate to real-life behaviour. The current study, therefore, examined neural RL signals in young, mildly to moderately depressed – but non-help-seeking and unmedicated – individuals and how these signals are associated with depressive symptoms and real-life motivated behaviour.
Individuals with symptoms along the depression continuum (n = 87) were recruited from the community. They performed an RL task during functional Magnetic Resonance Imaging and were assessed with the Experience Sampling Method (ESM), completing short questionnaires on emotions and behaviours up to 10 times/day for 15 days. Q-learning model-derived Reward Prediction Errors (RPEs) were examined in striatal areas, and subsequently associated with depressive symptoms and an ESM measure capturing (non-linearly) how anticipation of reward experience corresponds to actual reward experience later on.
Significant RPE signals were found in the striatum, insula, amygdala, hippocampus, frontal and occipital cortices. Region-of-interest analyses revealed a significant association between RPE signals and (a) self-reported depressive symptoms in the right nucleus accumbens (b = −0.017, p = 0.006) and putamen (b = −0.013, p = .012); and (b) the quadratic ESM variable in the left (b = 0.010, p = .010) and right (b = 0.026, p = 0.011) nucleus accumbens and right putamen (b = 0.047, p < 0.001).
Striatal RPE signals are disrupted along the depression continuum. Moreover, they are associated with reward-related behaviour in real-life, suggesting that real-life coupling of reward anticipation and engagement in rewarding activities might be a relevant target of psychological therapies for depression.
In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer’s disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach.
We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study.
Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705–0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706).
These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.
The course of illness in obsessive–compulsive disorder (OCD) varies significantly between patients. Little is known about factors predicting a chronic course of illness. The aim of this study is to identify factors involved in inducing and in maintaining chronicity in OCD.
The present study is embedded within the Netherlands Obsessive Compulsive Disorder Association (NOCDA) study, an ongoing multicenter naturalistic cohort study designed to identify predictors of long-term course and outcome in OCD. For this study, 270 subjects with a current diagnosis of OCD were included. Chronicity status at 2-year follow-up was regressed on a selection of baseline predictors related to OCD, to comorbidity and to stress and support.
Psychotrauma [odds ratio (OR) 1.98, confidence interval (CI) 1.22–3.22, p = 0.006], recent negative life events (OR 1.42, CI 1.01–2.01, p = 0.043), and presence of a partner (OR 0.28, CI 0.09–0.85, p = 0.025) influenced the risk of becoming chronic. Longer illness duration (OR 1.46, CI 1.08–1.96, p = 0.013) and higher illness severity (OR 1.09, CI 1.03–1.16, p = 0.003) increased the risk of remaining chronic.
External influences increase the risk of becoming chronic, whereas the factors involved in maintaining chronicity are illness-related. As the latter are potentially difficult to modify, treatment should be devoted to prevent chronicity from occurring in the first place. Therapeutic strategies aimed at alleviating stress and at boosting social support might aid in achieving this goal.
Alcohol use disorders and panic disorder co-occur at a rate that exceeds chance significantly. The underlying mechanism of alcoholism associated with anxiety has rarely been examined using experimental methodologies. The present study in healthy volunteers tested whether alcohol consumption reduces anxiety associated with a panic-challenge procedure (35% CO2 challenge).
The study design was placebo-controlled, double-blind, randomized. Eight healthy volunteers were enrolled; all subjects had an alcohol and a placebo oral intake according to a crossover design. After each consumption the subjects underwent the 35% CO2 challenge and a series of anxiety symptom assessments.
After the alcohol intake, the subjects presented a significant reduction in the anxiety state associated with the challenge procedure. The Panic Symptom List score is significantly lower after alcohol intake (P = 0.032), as compared with the placebo, and the Visual Analogue Anxiety Scale shows a trend to be lower after alcohol intake (P = 0.111).
Moderate doses of alcohol acutely decrease the response to a 35% CO2 challenge in healthy volunteers. These results lend support to the pharmacological anxiolytic effect of alcohol and suggest that this property may reinforce the drinking behaviour among those with high levels of anxiety.
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