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.

Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy.

Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.

Structural Connections of Functionally Defined Human Insular Subdivisions.

The organization of the human insular cortex has traditionally been considered as an anterior-posterior dichotomy, where anterior and posterior subdivisions have unique structural and functional connections. However, recent functional neuroimaging research proposes a tripartite organization where insular subdivisions have both unique and overlapping functional profiles. Studies examining unique profiles show that the dorsal anterior insula (dAI) has connections with frontal areas supporting higher-level cognitive processes, the ventral anterior insula (vAI) has connections with limbic areas supporting affective processes, and the posterior insula (PI) has connections with sensorimotor areas supporting interoceptive processes. Studies examining overlapping profiles demonstrate that all 3 subdivisions can also have similar functional profiles. The structural organization supporting a functional tripartite insula organization presenting with overlapping and unique connections is currently unknown. We used a large HARDI diffusion magnetic resonance imaging (MRI) dataset (n = 199) to demonstrate novel visualizations of insula white matter tracts supporting a tripartite structure-function insula organization. Overlapping connections of all 3 insula subdivisions consisted of association pathways (inferior fronto-occipital fasciculus, uncinate fasciculus, arcuate fasciculus) while unique connections included the corona radiata, subcortical-cortical tracts, and horizontal and u-shaped tracts. These results generally support a tripartite structure-function organization of the insular cortex, with subdivisions that exhibit both overlapping and unique connectivity profiles.

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder.

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Functional connectivity of human striatum: a resting state FMRI study.

Classically regarded as motor structures, the basal ganglia subserve a wide range of functions, including motor, cognitive, motivational, and emotional processes. Consistent with this broad-reaching involvement in brain function, basal ganglia dysfunction has been implicated in numerous neurological and psychiatric disorders. Despite recent advances in human neuroimaging, models of basal ganglia circuitry continue to rely primarily upon inference from animal studies. Here, we provide a comprehensive functional connectivity analysis of basal ganglia circuitry in humans through a functional magnetic resonance imaging examination during rest. Voxelwise regression analyses substantiated the hypothesized motor, cognitive, and affective divisions among striatal subregions, and provided in vivo evidence of a functional organization consistent with parallel and integrative loop models described in animals. Our findings also revealed subtler distinctions within striatal subregions not previously appreciated by task-based imaging approaches. For instance, the inferior ventral striatum is functionally connected with medial portions of orbitofrontal cortex, whereas a more superior ventral striatal seed is associated with medial and lateral portions. The ability to map multiple distinct striatal circuits in a single study in humans, as opposed to relying on meta-analyses of multiple studies, is a principal strength of resting state functional magnetic resonance imaging. This approach holds promise for studying basal ganglia dysfunction in clinical disorders.