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.

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.

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.

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.

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.

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.

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.

Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning.

A prominent theory in neuroscience suggests reward learning is driven by the discrepancy between a subject's expectation of an outcome and the actual outcome itself. Furthermore, it is postulated that midbrain dopamine neurons relay this mismatch to target regions including the ventral striatum. Using functional MRI (fMRI), we tested striatal responses to prediction errors for probabilistic classification learning with purely cognitive feedback. We used a version of the Rescorla-Wagner model to generate prediction errors for each subject and then entered these in a parametric analysis of fMRI activity. Activation in ventral striatum/nucleus-accumbens (Nacc) increased parametrically with prediction error for negative feedback. This result extends recent neuroimaging findings in reward learning by showing that learning with cognitive feedback also depends on the same circuitry and dopaminergic signaling mechanisms.

Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology.

The striatum has been widely implicated in cognition, but a precise understanding of its role remains elusive. Here we present converging evidence for the role of the striatum in feedback-based learning. In a prior functional imaging study, healthy controls showed striatal activity during a feedback-based learning task, which was decreased when the same task was learned without feedback. In the present study, we show that individuals with striatal dysfunction due to Parkinson's disease are impaired on the feedback-based task, but not on a non-feedback version of the same task. Parkinson's patients and controls also used different learning strategies depending on feedback structure. This study provides direct behavioural evidence from humans that cortico-striatal systems are necessary for feedback-based learning on a cognitive task. These findings also link between learning impairments in Parkinson's disease and the physiological and computational evidence for the role of midbrain dopaminergic systems in feedback processing.

Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning.

Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.

Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy.

Several current brain imaging techniques rest on the assumption of a tight coupling between neural activity and hemodynamic response. The nature of this neurovascular coupling, however, is not completely understood. There is some evidence for a decoupling of these processes at the onset of neural activity, which manifests itself as a momentary increase in the relative concentration of deoxyhemoglobin (HbR). The existence of this early component of the hemodynamic response function, however, is controversial, as it is inconsistently found. Near infrared spectroscopy (NIRS) allows quantification of levels of oxyhemoglobin (HbO(2)) and HbR during task performance in humans. We acquired NIRS data during performance of simple motor and visual tasks, using rapid-presentation event-related paradigms. Our results demonstrate that rapid, event-related NIRS can provide robust estimates of the hemodynamic response without artifacts due to low-frequency signal components, unlike data from blocked designs. In both the motor and visual data the onset of the increase in HbO(2) occurs before HbR decreases, and there is a poststimulus undershoot. Our results also show that total blood volume (HbT) drops before HbO(2) and undershoots baseline, raising a new issue for neurovascular models. We did not find early deoxygenation in the motor data using physiologically plausible values for the differential pathlength factor, but did find one in the visual data. We suggest that this difference, which is consistent with functional magnetic resonance imaging (fMRI) data, may be attributable to different capillary transit times in these cortices.

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.

Interactive memory systems in the human brain.

Learning and memory in humans rely upon several memory systems, which appear to have dissociable brain substrates. A fundamental question concerns whether, and how, these memory systems interact. Here we show using functional magnetic resonance imaging (FMRI) that these memory systems may compete with each other during classification learning in humans. The medial temporal lobe and basal ganglia were differently engaged across subjects during classification learning depending upon whether the task emphasized declarative or nondeclarative memory, even when the to-be-learned material and the level of performance did not differ. Consistent with competition between memory systems suggested by animal studies and neuroimaging, activity in these regions was negatively correlated across individuals. Further examination of classification learning using event-related FMRI showed rapid modulation of activity in these regions at the beginning of learning, suggesting that subjects relied upon the medial temporal lobe early in learning. However, this dependence rapidly declined with training, as predicted by previous computational models of associative learning.

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.

Material-specific lateralization in the medial temporal lobe and prefrontal cortex during memory encoding.

Numerous observations in patients with unilateral lesions of the medial temporal lobe (MTL) and the prefrontal cortex indicate that memory processes are lateralized according to content. Left-sided lesions interfere with verbal memory processes, whereas right-sided lesions interfere with visuospatial (non-verbal) memory processes. However, functional imaging studies have resulted in contradictory data, some studies showing lateralization in the prefrontal cortex determined by stage of processing (encoding versus retrieval) and others suggesting that lateralization is dependent on the type of material. Few studies have examined this issue in the MTL. In order to test the hypothesis that the lateralization of encoding processes in the MTL and frontal regions is dependent on the verbalizability of the material, we performed behavioural and functional imaging studies. We demonstrated differing verbalizabilities of three classes of non-verbal stimuli (scenes > faces > abstract patterns) using a dual-task verbal interference behavioural paradigm. A functional neuroimaging study of encoding was carried out using these three types of stimuli, plus words. During whole-brain functional MRI at 1.5 T, eight normal right-handed adults were presented with alternating blocks of novel and repeated stimuli under intentional memory encoding conditions. Verbal encoding resulted in left-lateralized activation of the inferior prefrontal cortex and the MTL. Pattern encoding activated the right inferior prefrontal cortex and the right MTL. Scenes and faces resulted in approximately symmetrical activation in both regions. The data indicate that the lateralization of encoding processes is determined by the verbalizability of stimuli.

Relations between the neural bases of dynamic auditory processing and phonological processing: evidence from fMRI.

Functional magnetic resonance imaging (fMRI) was used to examine how the brain responds to temporal compression of speech and to determine whether the same regions are also involved in phonological processes associated with reading. Recorded speech was temporally compressed to varying degrees and presented in a sentence verification task. Regions involved in phonological processing were identified in a separate scan using a rhyming judgment task with pseudowords compared to a lettercase judgment task. The left inferior frontal and left superior temporal regions (Broca's and Wernicke's areas), along with the right inferior frontal cortex, demonstrated a convex response to speech compression; their activity increased as compression increased, but then decreased when speech became incomprehensible. Other regions exhibited linear increases in activity as compression increased, including the middle frontal gyri bilaterally. The auditory cortices exhibited compression-related decreases bilaterally, primarily reflecting a decrease in activity when speech became incomprehensible. Rhyme judgments engaged two left inferior frontal gyrus regions (pars triangularis and pars opercularis), of which only the pars triangularis region exhibited significant compression-related activity. These results directly demonstrate that a subset of the left inferior frontal regions involved in phonological processing is also sensitive to transient acoustic features within the range of comprehensible speech.

Neural fate of seen and unseen faces in visuospatial neglect: a combined event-related functional MRI and event-related potential study.

To compare neural activity produced by visual events that escape or reach conscious awareness, we used event-related MRI and evoked potentials in a patient who had neglect and extinction after focal right parietal damage, but intact visual fields. This neurological disorder entails a loss of awareness for stimuli in the field contralateral to a brain lesion when stimuli are simultaneously presented on the ipsilateral side, even though early visual areas may be intact, and single contralateral stimuli may still be perceived. Functional MRI and event-related potential study were performed during a task where faces or shapes appeared in the right, left, or both fields. Unilateral stimuli produced normal responses in V1 and extrastriate areas. In bilateral events, left faces that were not perceived still activated right V1 and inferior temporal cortex and evoked nonsignificantly reduced N1 potentials, with preserved face-specific negative potentials at 170 ms. When left faces were perceived, the same stimuli produced greater activity in a distributed network of areas including right V1 and cuneus, bilateral fusiform gyri, and left parietal cortex. Also, effective connectivity between visual, parietal, and frontal areas increased during perception of faces. These results suggest that activity can occur in V1 and ventral temporal cortex without awareness, whereas coupling with dorsal parietal and frontal areas may be critical for such activity to afford conscious perception.

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.