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.

DISC1 is considered a susceptibility gene for schizophrenia and schizoaffective disorder, but little is known regarding the potential mechanisms through which it may confer increased risk. Given that DISC1 plays a role in cerebral cortex development, polymorphisms in this gene may have relevance for neurobiological models of schizophrenia that have implicated cortical deficits in its pathophysiology.

Working memory (WM) has been defined as the active maintenance and flexible updating of goal-relevant information in a form that has limited capacity and resists interference. Complex measures of WM recruit multiple subprocesses, making it difficult to isolate specific contributions of putatively independent subsystems. The present study was designed to determine whether neurophysiological indicators of proposed subprocesses of WM predict WM performance. We recruited 200 individuals defined by care-seeking status and measured neural responses using electroencephalography (EEG), while participants performed four WM tasks. We extracted spectral and time-domain EEG features from each task to quantify each of the hypothesized WM subprocesses: maintenance (storage of content), goal maintenance, and updating. We then used EEG measures of each subprocess as predictors of task performance to evaluate their contribution to WM. Significant predictors of WM capacity included contralateral delay activity and frontal theta, features typically associated with maintenance (storage of content) processes. In contrast, significant predictors of reaction time and its variability included contingent negative variation and the P3b, features typically associated with goal maintenance and updating. Broadly, these results suggest two principal dimensions that contribute to WM performance, tonic processes during maintenance contributing to capacity, and phasic processes during stimulus processing that contribute to response speed and variability. The analyses additionally highlight that reliability of features across tasks was greater (and comparable to that of WM performance) for features associated with stimulus processing (P3b and alpha), than with maintenance (gamma, theta and cross-frequency coupling).

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.

Both working memory (WM) and controlled (attention-mediated) semantic processing functions have been thought to operate as limited capacity systems, but the possible link between these processes has not been investigated. We found that increased WM load attenuated semantic priming (i.e., reduced the response time advantage for semantically primed relative to unprimed items) and changed fMRI signal intensities in brain regions usually associated with both WM (dorsolateral prefrontal cortex) and controlled semantic retrieval (inferior frontal gyrus [IFG], pars orbitalis). fMRI signal changes in dorsolateral prefrontal cortex were negatively correlated with signal changes in pars orbitalis. The findings suggest that controlled semantic processing and working memory share neural system resources.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

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.

There are limited data available regarding the effects of age and sex on discrete prefrontal gray and white matter volumes or posterior and anterior hippocampal volumes in healthy humans. Volumes of the superior frontal gyrus, anterior cingulate gyrus, and orbital frontal lobe were computed manually from contiguous magnetic resonance (MR) images in 83 (39M/44F) healthy humans (age range = 16-40) and segmented into gray and white matter. Volumes of the posterior and anterior hippocampal formation were also computed with reliable separation of the anterior hippocampal formation from the amygdala. There were significant age-by-tissue type interactions for the superior frontal gyrus and orbital frontal lobe such that gray matter within these regions correlated significantly and inversely with age. In contrast, no significant age effects were evident within regional white matter volumes. Analysis of hippocampal volumes indicated that men had larger volumes of the anterior, but not posterior hippocampal formation compared to women even following correction for total brain size. These data highlight age effects within discrete prefrontal cortical gray matter regions in young and middle aged healthy humans and suggest that the white matter comprising these regions may be more resistant to age effects. Furthermore, understanding the potential role of sex and age in mediating prefrontal cortical and hippocampal volumes may have strong relevance for psychiatric disorders such as schizophrenia that have implicated neurodevelopmental abnormalities within frontotemporal circuits in their pathogenesis.

Here, we present a large (n = 107,207) genome-wide association study (GWAS) of general cognitive ability ("g"), further enhanced by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with general cognitive ability. Results showed significant enrichment for genes causing Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis implicated the biological processes of neurogenesis and synaptic regulation, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker, and LY97241, a potassium channel inhibitor. Transcriptome-wide and epigenome-wide analysis revealed that the implicated loci were enriched for genes expressed across all brain regions (most strongly in the cerebellum). Enrichment was exclusive to genes expressed in neurons but not oligodendrocytes or astrocytes. Finally, we report genetic correlations between cognitive ability and disparate phenotypes including psychiatric disorders, several autoimmune disorders, longevity, and maternal age at first birth.

Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV" held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects.

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

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.

Neurofibromatosis Type 1 (NF1) is a genetic disorder that disrupts central nervous system development and neuronal function. Cognitively, NF1 is characterized by difficulties with executive control and visuospatial abilities. Little is known about the neural substrates underlying these deficits. The current study utilized Blood-Oxygen-Level-Dependent (BOLD) functional MRI (fMRI) to explore the neural correlates of spatial working memory (WM) deficits in patients with NF1.

Although excessive and compulsive shopping has been increasingly placed within the behavioral addiction paradigm in recent years, items in existing screens arguably do not assess the core criteria and components of addiction. To date, assessment screens for shopping disorders have primarily been rooted within the impulse-control or obsessive-compulsive disorder paradigms. Furthermore, existing screens use the terms 'shopping,' 'buying,' and 'spending' interchangeably, and do not necessarily reflect contemporary shopping habits. Consequently, a new screening tool for assessing shopping addiction was developed. Initially, 28 items, four for each of seven addiction criteria (salience, mood modification, conflict, tolerance, withdrawal, relapse, and problems), were constructed. These items and validated scales (i.e., Compulsive Buying Measurement Scale, Mini-International Personality Item Pool, Hospital Anxiety and Depression Scale, Rosenberg Self-Esteem Scale) were then administered to 23,537 participants (M age = 35.8 years, SD age = 13.3). The highest loading item from each set of four pooled items reflecting the seven addiction criteria were retained in the final scale, The Bergen Shopping Addiction Scale (BSAS). The factor structure of the BSAS was good (RMSEA = 0.064, CFI = 0.983, TLI = 0.973) and coefficient alpha was 0.87. The scores on the BSAS converged with scores on the Compulsive Buying Measurement Scale (CBMS; 0.80), and were positively correlated with extroversion and neuroticism, and negatively with conscientiousness, agreeableness, and intellect/imagination. The scores of the BSAS were positively associated with anxiety, depression, and low self-esteem and inversely related to age. Females scored higher than males on the BSAS. The BSAS is the first scale to fully embed shopping addiction within an addiction paradigm. A recommended cutoff score for the new scale and future research directions are discussed.

The purpose of this manuscript is to describe the PhenX RISING network and the site experiences in the implementation of PhenX measures into ongoing population-based genomic studies.

No abstract available