Often, there is diagnostic confusion between bipolar disorder (BD) and attention-deficit hyperactivity disorder (ADHD) in youth due to similar behavioral presentations. Both disorders have been implicated as having abnormal functioning in the prefrontal cortex; however, there may be subtle differences in the manner in which the prefrontal cortex functions in each disorder that could assist in their differentiation. Executive function is a construct thought to be a behavioral analogy to prefrontal cortex functioning. We provide a qualitative review of the literature on performance on executive function tasks for BD and ADHD in order to determine differences in task performance and neurocognitive profile. Our review found primary differences in executive function in the areas of interference control, working memory, planning, cognitive flexibility, and fluency. These differences may begin to establish a pediatric BD profile that provides a more objective means of differential diagnosis between BD and ADHD when they are not reliably distinguished by clinical diagnostic methods.

Introduction

Working Memory and Task-Switching are essential components of cognitive control, which underlies many symptoms evident across multiple neuropsychiatric disorders, including psychotic and mood disorders. Vulnerability to these disorders has a substantial genetic component, suggesting that clinically unaffected first-degree relatives may carry some vulnerability-related traits. Converging evidence from animal and human studies demonstrates that dopamine transmission, striatal and frontal brain regions, and attention and switching behaviors are essential components of a multilevel circuit involved in salience, and disruptions in that circuit may lead to features of psychosis. Yet, it is possible that unaffected relatives may also possess characteristics that protect against development of illness. We hypothesized that reduced switch cost in a cued task-switching task, may be a behavioral expression of this "resilience" phenotype that will be observable in unaffected relatives.

Methods

We tested a large community sample (n = 536) via the web, to assess different subcomponents of cognitive control, including task-switching and working memory, as well as risk-taking, among individuals who report having an affected relative with a psychotic or mood disorder.

Results

Healthy individuals with suspected genetic risk due to a self-reported familial history of a psychotic disorder demonstrated better task-switching performance compared to healthy people without a psychiatrically ill relative and those with a relative with a mood disorder. This result was specific to illness status and task domain, in that individuals with a personal history of depression or anxiety did not show improved task-switching performance, and this improvement was selective to task-switching and not seen in other putative cognitive control domains (working memory or risk taking).

Conclusions

Although this study has limitations and independent replication is needed, these preliminary findings suggest a potential avenue for understanding susceptibility to these disorders by highlighting possible protective as well as vulnerability-related aspects of risk phenotypes.

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9-30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

Introduction Understanding the relationship between brain and complex latent behavioral constructs like cognitive control will require an inordinate amount of data. Internet-based methods can rapidly and efficiently refine behavioral measures in very large samples that are needed for genetics and behavioral research. Cognitive control is a multifactorial latent construct that is considered to be an endophenotype in numerous neuropsychiatric disorders, including attention deficit/hyperactivity disorder (ADHD). While previous studies have demonstrated high correlations between Web- and lab-based scores, skepticism remains for its broad implementation. Methods Here, we promote a different approach by characterizing a completely Web-recruited and tested community family sample on measures of cognitive control. We examine the prevalence of attention deficit symptoms in an online community sample of adolescents, demonstrate familial correlations in cognitive control measures, and use construct validation techniques to validate our high-throughput assessment approach. Results A total of 1214 participants performed Web-based tests of cognitive control with over 200 parent-child pairs analyzed as part of the primary study aims. The data show a wide range of "subclinical" symptomatology in a web community sample of adolescents that supports a dimensional view of attention and also provide preliminary narrow-sense heritability estimates for commonly used working memory and response inhibition tests. Conclusions Finally, we show strong face and construct validity for these measures of cognitive control that generally exceeds the evidence required of new lab-based measures. We discuss these results and how broad implementation of this platform may allow us to uncover important brain-behavior relationships quickly and efficiently.

Previous work has shown that human adolescents may be hypersensitive to rewards, but it is not known which aspect of reward processing is responsible for this. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending on how value was modeled. This suggests that heightened dopaminergic prediction error responsivity contributes to adolescent reward seeking.

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

Background

Smokers exhibit an unusually high willingness to forgo a delayed reward of greater magnitude to receive a smaller, more immediate reward. The functional form of such "delay discounting" behavior is central to the discounting-based operationalization of impulsivity, and has implications for theories regarding the basis of steep discounting among smokers and treatment approaches to addiction.

Objectives

We examined the discounting behavior of current smokers, ex-smokers, and never-smokers to determine whether the functional form of discounting differs between these groups.

Methods

Participants completed a 27-item delay discounting questionnaire (25). We used finite mixture modeling in analyzing data as the product of two simultaneous data-generating processes (DGPs), a hyperbolic function and an exponential function, and compared results to a quasi-hyperbolic (QH) approximation, in a relatively large sample (n = 1205).

Results

Consistent with prior reports, current smokers exhibited steeper discounting relative to never-smokers across exponential, hyperbolic, and QH models. A mixture model provided significant support for exponential and hyperbolic discounting in the data, and both accounted for roughly 50% of the participants' choices. This mixture model showed a statistically significantly better fit to the data than the exponential, hyperbolic, or QH functions alone. Contrary to the prevailing view, current smokers were not more likely to discount hyperbolically than nonsmokers, and, thus, were not more prone to time-inconsistent discounting.

Conclusions

The results inform the interpretation of steep discounting among smokers and suggest that treatment approaches could be tailored to the type of discounting behavior that smokers exhibit.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.

Objective

Schizophrenia patients exhibit impaired working and episodic memory, but this may represent generalized impairment across memory modalities or performance deficits restricted to particular memory systems in subgroups of patients. Furthermore, it is unclear whether deficits are unique from those associated with other disorders.

Method

Healthy controls (n=1101) and patients with schizophrenia (n=58), bipolar disorder (n=49) and attention-deficit-hyperactivity-disorder (n=46) performed 18 tasks addressing primarily verbal and spatial episodic and working memory. Effect sizes for group contrasts were compared across tasks and the consistency of subjects' distributional positions across memory domains was measured.

Results

Schizophrenia patients performed poorly relative to the other groups on every test. While low to moderate correlation was found between memory domains (r=.320), supporting modularity of these systems, there was limited agreement between measures regarding each individual's task performance (ICC=.292) and in identifying those individuals falling into the lowest quintile (kappa=0.259). A general ability factor accounted for nearly all of the group differences in performance and agreement across measures in classifying low performers.

Conclusions

Pathophysiological processes involved in schizophrenia appear to act primarily on general abilities required in all tasks rather than on specific abilities within different memory domains and modalities. These effects represent a general shift in the overall distribution of general ability (i.e., each case functioning at a lower level than they would have if not for the illness), rather than presence of a generally low-performing subgroup of patients. There is little evidence that memory impairments in schizophrenia are shared with bipolar disorder and ADHD.

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.