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1.  High-throughput cognitive assessment using BrainTest.org: examining cognitive control in a family cohort 
Brain and Behavior  2013;3(5):552-561.
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.
doi:10.1002/brb3.158
PMCID: PMC3869983  PMID: 24392276
Cognitive control; inattention; spatial working memory; symptoms; WWW
2.  Differences in neural activation as a function of risk-taking task parameters 
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.
doi:10.3389/fnins.2013.00173
PMCID: PMC3786224  PMID: 24137106
functional impulsivity; dysfunctional impulsivity; risky decision-making; naturalistic risk-taking; ART; BART
3.  The Genetics of Cognitive Impairment in Schizophrenia: A Phenomic Perspective 
Trends in cognitive sciences  2011;15(9):428-435.
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.
doi:10.1016/j.tics.2011.07.002
PMCID: PMC3163827  PMID: 21816658
4.  Language network dysfunction as a predictor of outcome in youth at clinical high-risk for psychosis 
Schizophrenia research  2009;116(2-3):173.
Objectives
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.
Methods
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.
Results
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.
Conclusions
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.
doi:10.1016/j.schres.2009.09.042
PMCID: PMC2818263  PMID: 19861234
fMRI; schizophrenia; inferior frontal gyrus; psychosis prodrome; discourse; functional neuroimaging
5.  Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications 
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.
doi:10.3389/fnins.2011.00075
PMCID: PMC3118657  PMID: 21720520
predictive analysis; fMRI; high-dimensional regression; multivariate decoding; machine learning
6.  The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience 
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.
doi:10.3389/fninf.2011.00017
PMCID: PMC3167196  PMID: 21922006
ontology; informatics; neuroimaging; cognitive science
7.  A unique adolescent response to reward prediction errors 
Nature neuroscience  2010;13(6):669-671.
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.
doi:10.1038/nn.2558
PMCID: PMC2876211  PMID: 20473290
8.  Phenomics: The systematic study of phenotypes on a genome-wide scale 
Neuroscience  2009;164(1):30-42.
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.
doi:10.1016/j.neuroscience.2009.01.027
PMCID: PMC2760679  PMID: 19344640
phenotype; genetics; genomics; informatics; cognition; psychiatry
9.  Executive Function in Pediatric Bipolar Disorder and Attention-Deficit Hyperactivity Disorder: In Search of Distinct Phenotypic Profiles 
Neuropsychology Review  2010;20(1):103-120.
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.
doi:10.1007/s11065-009-9126-x
PMCID: PMC2834768  PMID: 20165924
Bipolar disorder; Attention-deficit hyperactivity disorder; Executive function; Prefrontal cortex; Neurocognitive
10.  Decoding Developmental Differences and Individual Variability in Response Inhibition Through Predictive Analyses Across Individuals 
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.
doi:10.3389/fnhum.2010.00047
PMCID: PMC2906202  PMID: 20661296
development; predictive analysis; fMRI; response inhibition; stop-signal
11.  Cognitive Ontologies for Neuropsychiatric Phenomics Research 
Cognitive neuropsychiatry  2009;14(4):419-450.
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.
doi:10.1080/13546800902787180
PMCID: PMC2752634  PMID: 19634038
Phenomics; Endophenotypes; Genetics; Cognition; Informatics

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