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.
Cognitive control; inattention; spatial working memory; symptoms; WWW
Sexual dimorphism in the brain and cognition is a topic of widespread interest. Many studies of sex differences have focused on visuospatial and verbal abilities but few studies have investigated sex differences in executive functions. We examined two key components of executive function—response inhibition and response monitoring—in healthy men (n=285) and women (n=346) performing the Stop-signal task. In this task, participants are required to make a key press to a stimulus, unless a tone is presented at some delay following the initial stimulus presentation; on these infrequent trials, participants are instructed to inhibit their planned response. Response inhibition was assessed with an estimate of the latency needed to inhibit a response (stop-signal reaction time), and response monitoring was measured by calculating the degree to which participants adjusted their reaction times based on the immediately preceding trial (e.g. speeding following correct trials and slowing following errors). There were no sex differences in overall accuracy or response inhibition but women showed greater sensitivity to trial history. Women sped up more than men following correct “Go” trials, and slowed down more than men following errors. These small but statistically significant effects (Cohen’s d=0.25–0.3) suggest more flexible adjustments in speed-accuracy trade-offs in women and greater cognitive flexibility associated with the responsive control of action.
Studies of adults with attention-deficit/hyperactivity disorder (ADHD) have suggested that they have deficient response inhibition, but findings concerning the neural correlates of inhibition in this patient population are inconsistent. We used the Stop-Signal task and functional magnetic resonance imaging (fMRI) to compare neural activation associated with response inhibition between adults with ADHD (N = 35) and healthy comparison subjects (N = 62), and in follow-up tests to examine the effect of current medication use and symptom severity. There were no differences in Stop-Signal task performance or neural activation between ADHD and control participants. Among the ADHD participants, however, significant differences were associated with current medication, with individuals taking psychostimulants (N = 25) showing less stopping-related activation than those not currently receiving psychostimulant medication (N = 10). Follow-up analyses suggested that this difference in activation was independent of symptom severity. These results provide evidence that deficits in inhibition-related neural activation persist in a subset of adult ADHD individuals, namely those individuals currently taking psychostimulants. These findings help to explain some of the disparities in the literature, and advance our understanding of why deficits in response inhibition are more variable in adult, as compared with child and adolescent, ADHD patients.
Inhibitory control; Hyperactivity; Psychostimulants; Functional magnetic resonance imaging (fMRI); Adults; Stop-Signal task
The Stop-signal task (SST), in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision-making, a drift diffusion model of simple decisions was fitted to SST data from Go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the Go stimulus correlated with greater activation in the right frontal pole for both Go and Stop trials. On Stop trials stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and basal ganglia. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control, and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.
drift-diffusion model; fMRI; Individual differences; inhibitory control; Stop signal task
The Barratt Impulsiveness Scale Version 11 (BIS-11; Patton, Stanford & Barratt, 1995) is a gold-standard measure that has been influential in shaping current theories of impulse control, and has played a key role in studies of impulsivity and its biological, psychological, and behavioral correlates. Psychometric research on the structure of the BIS-11, however, has been scant. We therefore applied exploratory and confirmatory factor analyses to data collected using the BIS-11 in a community sample (N = 691). Our goal was to test four theories of the BIS-11 structure: (a) a unidimensional model; (b) a six correlated first-order factor model, (c) a three second-order factor model, and (d) a bifactor model. Among the problems identified were: (a) low or near-zero correlations of some items with others; (b) highly redundant content of numerous item pairs; (c) items with salient cross-loadings in multidimensional solutions; and ultimately; (d) poor fit to confirmatory models. We conclude that use of the BIS-11 total score as reflecting individual differences on a common dimension of impulsivity presents challenges in interpretation. Also, the theory that the BIS-11 measures three subdomains of impulsivity (attention, motor, and non-planning) was not empirically supported. A two-factor model is offered as an alternative multidimensional structural representation.
Barratt Impulsiveness Scale Version 11; structural validity; impulsivity; confirmatory factor analysis
Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART), which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analog Risk Taking (BART) task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity (DI) and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step toward characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.
functional impulsivity; dysfunctional impulsivity; risky decision-making; naturalistic risk-taking; ART; BART
Cognitive impairments are central to schizophrenia and may mark underlying biological dysfunction, but efforts to detect genetic associations for schizophrenia or cognitive phenotypes have been disappointing. Phenomics strategies emphasizing simultaneous study of multiple phenotypes across biological scales may help, particularly if the high heritabilities of schizophrenia and cognitive impairments are due to large numbers of genetic variants with small effect. Convergent evidence is reviewed, and a new collaborative knowledgebase – CogGene – is introduced to share data about genetic associations with cognitive phenotypes, and enable users to meta-analyze results interactively. CogGene data demonstrate the need for larger studies with broader representation of cognitive phenotypes. Given that meta-analyses will likely be necessary to detect the small association signals linking the genome and cognitive phenotypes, CogGene or similar applications will be needed to enable collaborative knowledge aggregation and specify true effects.
Language processing abnormalities are a hallmark feature of schizophrenia. Yet, no study to date has investigated underlying neural networks associated with discourse processing in adolescents at clinical high risk (CHR) for developing psychosis.
Forty CHR youth and 24 demographically comparable healthy controls underwent functional magnetic resonance imaging while performing a naturalistic discourse processing paradigm. We assessed differences in blood oxygenation level-dependent (BOLD) activity between task conditions (Topic Maintenance vs. Reasoning) and between groups. Furthermore, we examined the association of regional brain activity with symptom severity and social outcome at follow-up, 6 to 24 months after the scan.
Relative to controls, CHR participants showed increased neural activity in a network of language-associated brain regions, including the medial prefrontal cortex bilaterally, left inferior frontal (LIFG; BA44/45, 47) and middle temporal gyri, and the anterior cingulate (BA24&32). Further, increased activity in the superior temporal gyrus (STG), caudate, and LIFG distinguished those who subsequently developed psychosis. Within the CHR sample, severity of positive formal thought disorder at follow-up was positively correlated with signal change in the LIFG, superior frontal gyrus, and inferior/middle temporal gyri, whereas social outcome was inversely correlated with signal change in the LIFG and anterior cingulate.
These findings are consistent with a neural inefficiency hypothesis in those at greatest risk for psychosis, and additionally suggest that baseline activation differences may predict symptomatic and functional outcome. These results highlight the need to further investigate the neural systems involved in conversion to psychosis, and how language disruption changes over time in at-risk adolescents.
fMRI; schizophrenia; inferior frontal gyrus; psychosis prodrome; discourse; functional neuroimaging
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.
predictive analysis; fMRI; high-dimensional regression; multivariate decoding; machine learning
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.
ontology; informatics; neuroimaging; cognitive science
Previous work has demonstrated that human adolescents may be hypersensitive to rewards; it is unknown which aspect of reward processing this reflects. 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 upon how value was modeled. This suggests that one contributor to adolescent reward-seeking may be heightened dopaminergic prediction error responsivity.
Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping –the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the NIH Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledgebases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.
phenotype; genetics; genomics; informatics; cognition; psychiatry
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.
Bipolar disorder; Attention-deficit hyperactivity disorder; Executive function; Prefrontal cortex; Neurocognitive
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.
development; predictive analysis; fMRI; response inhibition; stop-signal
Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical “tree” structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive “test speciation” is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be “mated” productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualization of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalize inference about cognitive concepts in behavioral and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.
Phenomics; Endophenotypes; Genetics; Cognition; Informatics