The effects of life stress and neural learning signals on fluid intelligence

Impairments to executive function in emerging adults with Huntington disease

BACKGROUND AND OBJECTIVES

The clinical diagnosis of Huntington disease (HD) is typically made once motor symptoms and chorea are evident. Recent reports highlight the onset of cognitive and psychiatric symptoms before motor manifestations. These findings support further investigations of cognitive function across the lifespan of HD sufferers.

METHODS

To assess cognitive symptoms in the developing brain, we administered assessments from the National Institutes of Health Toolbox Cognitive Battery, an age-appropriate cognitive assessment with population norms, to a cohort of children, adolescents and young adults with (gene-expanded; GE) and without (gene-not-expanded; GNE) the trinucleotide cytosine, adenine, guanine (CAG) expansion in the Huntingtin gene. These five assessments that focus on executive function are well validated and form a composite score, with population norms. We modelled these scores across age, and CAP score to estimate the slope of progression, comparing these results to motor symptoms.

RESULTS

We find significant deficits in the composite measure of executive function in GE compared with GNE participants. GE participant performance on working memory was significantly lower compared with GNE participants. Modelling these results over age suggests that these deficits occur as early as 18 years of age, long before motor manifestations of HD.

CONCLUSIONS

This work provides strong evidence that impairments in executive function occur as early as the second decade of life, well before anticipated motor onset. Future investigations should delineate whether these impairments in executive function are due to abnormalities in neurodevelopment or early sequelae of a neurodegenerative process.

Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis

BACKGROUND

Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples.

METHODS

Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals.

RESULTS

There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology.

CONCLUSIONS

Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.

The IMAGEN study: a decade of imaging genetics in adolescents

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype 'drug use' to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

(Neuro)therapeutic Approaches in the Field of Alcohol Use Disorders

Purpose of Review

Alcohol use disorder (AUD) is a burdening chronic condition that is characterized by high relapse rates despite severe negative consequences. There has been a recent emergence of interest in (neuro)therapeutic intervention strategies that largely involve the detrimental change in mechanisms linked to addiction disorders. Most prominently, the latter include habitual decision-making, cue-induced behavioral tendencies, as well as the amplifying effects of stressful events on drinking behavior. This article discusses these learning mechanisms and modification thereof as possible targets of (neuro)therapeutic interventions for AUD.

Recent Findings

Psychological therapies that target dysregulated neurocognitive processes underlying addictive behavior may hold promise as effective treatments for AUD.

Summary

Despite the progression in psychological and neuroscience research in the field of AUD, many behavioral interventions fail to systematically integrate and apply such findings into treatment development. Future research should focus on the targeted modification of the aforementioned processes.

Addiction as Learned Behavior Patterns

Individuals with substance use disorders (SUDs) have to cope with drug-related cues and contexts which can affect instrumental drug seeking, as shown with Pavlovian-to-instrumental transfer (PIT) tasks among humans and animals. Our review addresses two potential mechanisms that may contribute to habitual or even compulsive drug seeking and taking. One mechanism is represented by Pavlovian and PIT effects on drug intake. The other is a shift from goal-directed to habitual drug intake, which can be accessed via model-based versus model-free decision-making in respective learning tasks. We discuss the impact of these learning mechanisms on drug consumption. First, we describe how Pavlovian and instrumental learning mechanisms interact in drug addiction. Secondly, we address the effects of acute and chronic stress exposure on behavioral and neural PIT effects in alcohol use disorder (AUD). Thirdly, we discuss how these learning mechanisms and their respective neurobiological correlates can contribute to losing versus regaining control over drug intake. Utilizing mobile technology (mobile applications on smartphones including games that measure learning mechanisms, activity bracelets), computational models, and real-world data may help to better identify patients with a high relapse risk and to offer targeted behavioral and pharmacotherapeutic interventions for vulnerable patients.

Epigenetic variance in dopamine D2 receptor: a marker of IQ malleability?

Genetic and environmental factors both contribute to cognitive test performance. A substantial increase in average intelligence test results in the second half of the previous century within one generation is unlikely to be explained by genetic changes. One possible explanation for the strong malleability of cognitive performance measure is that environmental factors modify gene expression via epigenetic mechanisms. Epigenetic factors may help to understand the recent observations of an association between dopamine-dependent encoding of reward prediction errors and cognitive capacity, which was modulated by adverse life events. The possible manifestation of malleable biomarkers contributing to variance in cognitive test performance, and thus possibly contributing to the "missing heritability" between estimates from twin studies and variance explained by genetic markers, is still unclear. Here we show in 1475 healthy adolescents from the IMaging and GENetics (IMAGEN) sample that general IQ (gIQ) is associated with (1) polygenic scores for intelligence, (2) epigenetic modification of DRD2 gene, (3) gray matter density in striatum, and (4) functional striatal activation elicited by temporarily surprising reward-predicting cues. Comparing the relative importance for the prediction of gIQ in an overlapping subsample, our results demonstrate neurobiological correlates of the malleability of gIQ and point to equal importance of genetic variance, epigenetic modification of DRD2 receptor gene, as well as functional striatal activation, known to influence dopamine neurotransmission. Peripheral epigenetic markers are in need of confirmation in the central nervous system and should be tested in longitudinal settings specifically assessing individual and environmental factors that modify epigenetic structure.

Eye Movements and Cognitive Strategy in a Fluid Intelligence Test: Item Type Analysis

Eye movements help to infer the cognitive strategy that a person uses in fluid intelligence tests. However, intelligence tests demand different relations/rules tokens to be solved, such as rule direction, which is the continuation, variation or overlay of geometric figures in the matrix of the intelligence test. The aim of this study was to understand whether eye movements could predict the outcome of an intelligence test and in the rule item groups. Furthermore, we sought to identify which measure is best for predicting intelligence test scores and to understand if the rule item groups use the same strategy. Accordingly, 34 adults completed a computerized intelligence test with an eye-tracking device. The toggling rate, that is, the number of toggles on each test item equalized by the item latency explained 45% of the variance of the test scores and a significant amount of the rule tokens item groups. The regression analyses also indicated toggling rate as the best measure for predicting the score and that all the rule tokens seem to respect the same strategy. No correlation or difference were found between baseline pupil size and fluid intelligence. Wiener Matrizen-Test 2 was demonstrated to be a good instrument for the purpose of this study. Finally, the implications of these findings for an understanding of cognition are discussed.

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.