[Show abstract][Hide abstract] ABSTRACT: Transdisciplinary research is a rapidly expanding part of science and engineering, demanding new methods for connecting results across fields. In biomedicine for example, modeling complex biological systems requires linking knowledge across multiple levels of science, from genes to disease. The move to multilevel research requires new strategies; in this paper we present path knowledge discovery, a novel methodology for linking published research findings. Path knowledge discovery consists of two integral tasks: 1) association path mining among concepts in a multipart lexicon that crosses disciplines, and 2) fine-granularity knowledge-based content retrieval along the path(s) to permit deeper analysis. Implementing this methodology has required development of innovative measures of association strength for pairwise associations, as well as the strength for sequences of associations, in addition to powerful lexicon-based association expansion to increase the scope of matching. In our discussions, we describe the validation of the methodology using a published heritability study from cognition research, and we obtain comparable results. We show how path knowledge discovery can greatly reduce a domain expert's time (by several orders of magnitude) when searching and gathering knowledge from the published literature, and can facilitate derivation of interpretable results.
[Show abstract][Hide abstract] ABSTRACT: Background / Purpose:
Schizophrenia patients exhibit impaired working and episodic memory that relates to functioning and prognosis. However, the relationship of deficits in schizophrenia to the distribution of performance across memory subsystems in controls and other psychiatric disorders is unclear.
There are consistent, moderate to large impairments across memory measures in schizophrenia. While there is some consistency across measures as to individual levels of performance, this is primarily accounted for by a single generalized cognitive factor (PC1). This same generalized cognitive factor accounts for nearly all of the group differences on performance across tasks.
69th Society of Biological Psychiatry Annual Meeting 2014; 06/2014
[Show abstract][Hide abstract] ABSTRACT: 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.
British Journal of Psychology 05/2014; 105(2):254-72. DOI:10.1111/bjop.12034 · 2.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.
Proceedings of the National Academy of Sciences 02/2014; 111(7):2470-5. DOI:10.1073/pnas.1321728111 · 9.67 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The stop-signal task, 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 stop-signal task 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 BG. 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.
[Show abstract][Hide abstract] ABSTRACT: 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.
Frontiers in Neuroscience 09/2013; 7(7):173. DOI:10.3389/fnins.2013.00173 · 3.66 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
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.
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.
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.
Brain and Behavior 09/2013; 3(5):552-61. DOI:10.1002/brb3.158 · 2.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Reports an error in "Multilevel Models From Biology to Psychology: Mission Impossible" by Robert M. Bilder, Andrew S. Howe and Fred w. Sabb (Journal of Abnormal Psychology, Advanced Online Publication, May 6, 2013, np). The name of author Andrew G. Howe was misspelled as Andrew S. Howe. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2013-15712-001.) Systematic efforts are underway to address major flaws in the current diagnostic taxonomy of mental disorders, fostering hope that a new nosology might be based on brain biology. The National Institute of Mental Health Research Domains Criteria (RDoC) initiative aims to redefine mental illness leveraging information that spans molecular to behavioral levels of analysis. Major effort is still needed to forge multilevel conceptual and measurement models capable of representing knowledge within and across these levels. The development of such models may help refine and share complex hypotheses, and reduce the risk of replacing the current taxonomy with dimensions and/or categories that manifest little incremental biological validity. To create useful models we need to define concepts, relations among concepts, and links to supporting evidence. Some methods already enable representation of concepts and measures at the levels of behavioral and basic biological processes, but a major gap at the level of neural circuitry must be bridged to link basic biological and behavioral levels. We provide a schematic framework, using as an example the representation of selected "working memory" concepts and evidence across multiple levels of analysis as these have been described in the RDoC Workshops. This example illustrates multiple challenges and some possible solutions that may help clarify the aims of individual research projects and enable integration of diverse efforts on RDoC and related initiatives. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
[Show abstract][Hide abstract] ABSTRACT: Systematic efforts are underway to address major flaws in the current diagnostic taxonomy of mental disorders, fostering hope that a new nosology might be based on brain biology. The National Institute of Mental Health Research Domains Criteria (RDoC) initiative aims to redefine mental illness leveraging information that spans molecular to behavioral levels of analysis. Major effort is still needed to forge multilevel conceptual and measurement models capable of representing knowledge within and across these levels. The development of such models may help refine and share complex hypotheses, and reduce the risk of replacing the current taxonomy with dimensions and/or categories that manifest little incremental biological validity. To create useful models we need to define concepts, relations among concepts, and links to supporting evidence. Some methods already enable representation of concepts and measures at the levels of behavioral and basic biological processes, but a major gap at the level of neural circuitry must be bridged to link basic biological and behavioral levels. We provide a schematic framework, using as an example the representation of selected "working memory" concepts and evidence across multiple levels of analysis as these have been described in the RDoC Workshops. This example illustrates multiple challenges and some possible solutions that may help clarify the aims of individual research projects and enable integration of diverse efforts on RDoC and related initiatives. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
[Show abstract][Hide abstract] ABSTRACT: 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 4 theories of the BIS-11 structure: (a) a unidimensional model, (b) a 6 correlated first-order factor model, (c) a 3 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 3 subdomains of impulsivity (attention, motor, and nonplanning) was not empirically supported. A 2-factor model is offered as an alternative multidimensional structural representation. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
[Show abstract][Hide abstract] ABSTRACT: 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.
Frontiers in Neuroinformatics 09/2011; 5(17):17. DOI:10.3389/fninf.2011.00017 · 3.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Cognitive impairments are central to schizophrenia and could 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 might 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 probably 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.
[Show abstract][Hide abstract] ABSTRACT: 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.
Frontiers in Neuroscience 06/2011; 5:75. DOI:10.3389/fnins.2011.00075 · 3.66 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
Frontiers in Human Neuroscience 07/2010; 4:47. DOI:10.3389/fnhum.2010.00047 · 3.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Cognitive Atlas is a collaborative knowledge-building project that aims to develop an ontology that characterizes the current conceptual framework among researchers in cognitive science and neuroscience. The project objectives from the beginning focused on usability, simplicity, and utility for end users. Support for Semantic Web technologies was also a priority in order to support interoperability with other neuroscience projects and knowledge bases. Current off-the-shelf semantic web or semantic wiki technologies, however, do not often lend themselves to simple user interaction designs for non-technical researchers and practitioners; the abstract nature and complexity of these systems acts as point of friction for user interaction, inhibiting usability and utility. Instead, we take an alternate interaction design approach driven by user centered design processes rather than a base set of semantic technologies. This paper reviews the initial two rounds of design and development of the Cognitive Atlas system, including interactive design decisions and their implementation as guided by current industry practices for the development of complex interactive systems.
[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: 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.