GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium.

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10-8). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium.

This corrects the article DOI: 10.1038/mp.2016.244.

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework.

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.

Six problems for causal inference from fMRI.

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

A data resource from concurrent intracranial stimulation and functional MRI of the human brain.

Mapping the causal effects of one brain region on another is a challenging problem in neuroscience that we approached through invasive direct manipulation of brain function together with concurrent whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

Recovering meaning: left prefrontal cortex guides controlled semantic retrieval.

Prefrontal cortex plays a central role in mnemonic control, with left inferior prefrontal cortex (LIPC) mediating control of semantic knowledge. One prominent theory posits that LIPC does not mediate semantic retrieval per se, but rather subserves the selection of task-relevant knowledge from amidst competing knowledge. The present event-related fMRI study provides evidence for an alternative hypothesis: LIPC guides controlled semantic retrieval irrespective of whether retrieval requires selection against competing representations. With selection demands held constant, LIPC activation increased with semantic retrieval demands and with the level of control required during retrieval. LIPC mediates a top-down bias signal that is recruited to the extent that the recovery of meaning demands controlled retrieval. Selection may reflect a specific instantiation of this mechanism.

Microstructure of temporo-parietal white matter as a basis for reading ability: evidence from diffusion tensor magnetic resonance imaging.

Diffusion tensor magnetic resonance imaging (MRI) was used to study the microstructural integrity of white matter in adults with poor or normal reading ability. Subjects with reading difficulty exhibited decreased diffusion anisotropy bilaterally in temporoparietal white matter. Axons in these regions were predominantly anterior-posterior in direction. No differences in T1-weighted MRI signal were found between poor readers and control subjects, demonstrating specificity of the group difference to the microstructural characteristics measured by diffusion tensor imaging (DTI). White matter diffusion anisotropy in the temporo-parietal region of the left hemisphere was significantly correlated with reading scores within the reading-impaired adults and within the control group. The anisotropy reflects microstructure of white matter tracts, which may contribute to reading ability by determining the strength of communication between cortical areas involved in visual, auditory, and language processing.

A phenome-wide examination of neural and cognitive function.

This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (43 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Hemispheric asymmetries and individual differences in visual concept learning as measured by functional MRI.

Dynamic changes in brain regions active while learning novel visual concepts were examined in humans using functional magnetic resonance imaging. Participants learned to distinguish between exemplars of two categories, formed as distortions of different unseen prototype stimuli. Regions of the right hemisphere (dorsolateral prefrontal and inferior parietal areas) were active early in learning and throughout task performance, whereas homologous portions of the left hemisphere were active only in later stages of learning. Left dorsolateral prefrontal activation was found only in participants who showed superior conceptual learning. Such a progression from initial right-hemisphere processing of specific instances to bilateral activity as left-hemisphere conceptual processes are recruited may underlie the development of many forms of visual knowledge.

Material-specific lateralization of prefrontal activation during episodic encoding and retrieval.

Although numerous neuroimaging studies have examined the functional neuroanatomy supporting episodic memory for verbal material, there have been few investigations of non-verbal episodic encoding and retrieval. We used fMRI to directly compare prefrontal activation elicited by verbal and non-verbal material during encoding and during retrieval. Regardless of the mnemonic operation (encoding/retrieval), inferior prefrontal activation lateralized based on material type. Verbal encoding and retrieval resulted in greater left inferior prefrontal activation, whereas non-verbal encoding and retrieval resulted in greater right inferior prefrontal activation. The similarity between inferior prefrontal activity during encoding and during retrieval indicates that these mnemonic operations depend on shared processes mediated by inferior prefrontal regions.

Disrupted neural responses to phonological and orthographic processing in dyslexic children: an fMRI study.

Developmental dyslexia, characterized by difficulty in reading, has been associated with phonological and orthographic processing deficits. fMRI was performed on dyslexic and normal-reading children (8-12 years old) during phonological and orthographic tasks of rhyming and matching visually presented letter pairs. During letter rhyming, both normal and dyslexic reading children had activity in left frontal brain regions, whereas only normal-reading children had activity in left temporo-parietal cortex. During letter matching, normal-reading children showed activity throughout extrastriate cortex, especially in occipito-parietal regions, whereas dyslexic children had little activity in extrastriate cortex during this task. These results indicate dyslexia may be characterized in childhood by disruptions in the neural bases of both phonological and orthographic processes important for reading.

Striatal activation during acquisition of a cognitive skill.

The striatum is thought to play an essential role in the acquisition of a wide range of motor, perceptual, and cognitive skills, but neuroimaging has not yet demonstrated striatal activation during nonmotor skill learning. Functional magnetic resonance imaging was performed while participants learned probabilistic classification, a cognitive task known to rely on procedural memory early in learning and declarative memory later in learning. Multiple brain regions were active during probabilistic classification compared with a perceptual-motor control task, including bilateral frontal cortices, occipital cortex, and the right caudate nucleus in the striatum. The left hippocampus was less active bilaterally during probabilistic classification than during the control task, and the time course of this hippocampal deactivation paralleled the expected involvement of medial temporal structures based on behavioral studies of amnesic patients. Findings provide initial evidence for the role of frontostriatal systems in normal cognitive skill learning.

Functional anatomy of long-term memory.

Memory in the brain is organized into multiple memory systems that perform different memory functions and have different neurologic substrates. Declarative memory involves conscious memory for facts and events. The medial temporal lobe and structures in the diencephalon are essential in the establishment of new declarative memories, and these memory traces are finally stored in domain-specific regions of the cerebral cortex. The frontal lobe and basal ganglia are important in some forms of declarative memory that require reasoning about the contents of memory. Nondeclarative forms of memory (including skill learning, repetition priming, and classical conditioning) do not involve conscious recollection and are measured through changes in the way in which tasks are performed. These forms of memory rely upon the cerebral cortex, basal ganglia, and cerebellum.

Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy?

We have measured the changes in oxy-haemoglobin and deoxy-haemoglobin in the adult human brain during a brief finger tapping exercise using near-infrared spectroscopy (NIRS). The cerebral metabolic rate of oxygen (CMRO2) can be estimated from these NIRS data provided certain model assumptions. The change in CMRO2 is related to changes in the total haemoglobin concentration, deoxy-haemoglobin concentration and blood flow. As NIRS does not provide a measure of dynamic changes in blood flow during brain activation, we relied on a Windkessel model that relates dynamic blood volume and flow changes, which has been used previously for estimating CMRO2 from functional magnetic resonance imaging (fMRI) data. Because of the partial volume effect we are unable to quantify the absolute changes in the local brain haemoglobin concentrations with NIRS and thus are unable to obtain an estimate of the absolute CMRO2 change. An absolute estimate is also confounded by uncertainty in the flow-volume relationship. However, the ratio of the flow change to the CMRO2 change is relatively insensitive to these uncertainties. For the linger tapping task, we estimate a most probable flow-consumption ratio ranging from 1.5 to 3 in agreement with previous findings presented in the literature, although we cannot exclude the possibility that there is no CMRO2 change. The large range in the ratio arises from the large number of model parameters that must be estimated from the data. A more precise estimate of the flow-consumption ratio will require better estimates of the model parameters or flow information, as can be provided by combining NIRS with fMRI.

The relationship between skill learning and repetition priming: experimental and computational analyses.

Skill learning and repetition priming are considered by some to be supported by separate memory systems. The authors examined the relationship between skill learning and priming in 3 experiments using a digit entering task, in which participants were presented with unique and repeated 5-digit strings with controlled sequential structure. Both skill learning and priming were observed across a wide range of skill levels. Performance reflected the effects of learning at 3 different levels of stimulus structure, calling into question a binary dichotomy between item-specific priming and general skill learning. Two computational models were developed which demonstrated that previous dissociations between skill learning and priming can occur within a single memory system. The experimental and computational results are interpreted as suggesting that skill learning and priming should be viewed as 2 aspects of a single incremental learning mechanism.

On testing for stochastic dissociations.

Methods for examining stochastic relationships have been proposed as powerful ways to dissociate different underlying psychological processes, but a number of problems have undermined conclusions based upon these methods. These testing methods and criticisms are reviewed, and the statistical methods for the testing of stochastic dependence are examined using computer simulation. With each of the methods examined, there were difficulties dealing with certain situations that are likely to occur in experiments examining dependence between successive tests. Examination also showed that the sample sizes of some previous studies were insufficient for findings of moderate amounts of dependence, calling some conclusions of stochastic independence into question. The results of the studies presented here suggest that testing for statistical dependence is a statistically perilous technique, but they also suggest several ways in which dedicated users of this form of testing can strengthen its application.

What is the mechanism for fluency in successive recognition?

There are a number of reasons to believe that processing fluency may affect successive recognition judgements, but evidence about the mechanism for these effects is currently lacking. This study used a successive task design to examine whether subjective ease might underlie effects of fluency on recognition. At study subjects performed lexical decisions; in a subsequent test with studied and new items, subjects performed lexical decisions followed immediately by recognition or ease judgments. In a previous study we used that process dissociation procedure to show that recognition in a similar task was largely based upon fluency. In the present study, successive recognition judgments interfered with lexical decision performance to a greater degree than did ease judgments, suggesting that the recognition judgment was not automatic and involved processes additional to the judgment of ease. The data suggest that the fluency involved in successive recognition is more complex than a subjective judgment of ease of processing. One possible mechanism for fluency in recognition may be based upon reductions in the orientation of attention that accompany item repetition.

Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence.

Refining phenotypes for the study of neuropsychiatric disorders is of paramount importance in neuroscience. Poor phenotype definition provides the greatest obstacle for making progress in disorders like schizophrenia, bipolar disorder, Attention Deficit/Hyperactivity Disorder (ADHD), and autism. Using freely available informatics tools developed by the Consortium for Neuropsychiatric Phenomics (CNP), we provide a framework for defining and refining latent constructs used in neuroscience research and then apply this strategy to review known genetic contributions to memory and intelligence in healthy individuals. This approach can help us begin to build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases. These results are available online through the http://www.phenowiki.org database. Further work needs to be done in order to provide consensus-building applications for the broadly defined constructs used in neuroscience research.

Phenomics: the systematic study of phenotypes on a genome-wide scale.

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 National Institutes of Health 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 knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.