F. Xavier Castellanos

Toward discovery science of human brain function

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individuals “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individuals functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes

Research on attention-deficit/hyperactivity disorder (ADHD), a highly prevalent and controversial condition, has, for the most part, been descriptive and atheoretical. The imperative to discover the genetic and environmental risk factors for ADHD is motivating the search for quantifiable intermediate constructs, termed endophenotypes. In this selective review, we conclude that such endophenotypes should be solidly grounded in the neurosciences. We propose that three such endophenotypes — a specific abnormality in reward-related circuitry that leads to shortened delay gradients, deficits in temporal processing that result in high intrasubject intertrial variability, and deficits in working memory — are most amenable to integrative collaborative approaches that aim to uncover the causes of ADHD.

Functional connectivity of default mode network components: Correlation, anticorrelation, and causality

The default mode network (DMN), based in ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC), exhibits higher metabolic activity at rest than during performance of externally oriented cognitive tasks. Recent studies have suggested that competitive relationships between the DMN and various task‐positive networks involved in task performance are intrinsically represented in the brain in the form of strong negative correlations (anticorrelations) between spontaneous fluctuations in these networks. Most neuroimaging studies characterize the DMN as a homogenous network, thus few have examined the differential contributions of DMN components to such competitive relationships. Here, we examined functional differentiation within the DMN, with an emphasis on understanding competitive relationships between this and other networks. We used a seed correlation approach on resting‐state data to assess differences in functional connectivity between these two regions and their anticorrelated networks. While the positively correlated networks for the vmPFC and PCC seeds largely overlapped, the anticorrelated networks for each showed striking differences. Activity in vmPFC negatively predicted activity in parietal visual spatial and temporal attention networks, whereas activity in PCC negatively predicted activity in prefrontal‐based motor control circuits. Granger causality analyses suggest that vmPFC and PCC exert greater influence on their anticorrelated networks than the other way around, suggesting that these two default mode nodes may directly modulate activity in task‐positive networks. Thus, the two major nodes comprising the DMN are differentiated with respect to the specific brain systems with which they interact, suggesting greater heterogeneity within this network than is commonly appreciated. Hum Brain Mapp 30:625–637, 2009.

Precuneus shares intrinsic functional architecture in humans and monkeys

Evidence from macaque monkey tracing studies suggests connectivity-based subdivisions within the precuneus, offering predictions for similar subdivisions in the human. Here we present functional connectivity analyses of this region using resting-state functional MRI data collected from both humans and macaque monkeys. Three distinct patterns of functional connectivity were demonstrated within the precuneus of both species, with each subdivision suggesting a discrete functional role: (i) the anterior precuneus, functionally connected with the superior parietal cortex, paracentral lobule, and motor cortex, suggesting a sensorimotor region; (ii) the central precuneus, functionally connected to the dorsolateral prefrontal, dorsomedial prefrontal, and multimodal lateral inferior parietal cortex, suggesting a cognitive/associative region; and (iii) the posterior precuneus, displaying functional connectivity with adjacent visual cortical regions. These functional connectivity patterns were differentiated from the more ventral networks associated with the posterior cingulate, which connected with limbic structures such as the medial temporal cortex, dorsal and ventromedial prefrontal regions, posterior lateral inferior parietal regions, and the lateral temporal cortex. Our findings are consistent with predictions from anatomical tracer studies in the monkey, and provide support that resting-state functional connectivity (RSFC) may in part reflect underlying anatomy. These subdivisions within the precuneus suggest that neuroimaging studies will benefit from treating this region as anatomically (and thus functionally) heterogeneous. Furthermore, the consistency between functional connectivity networks in monkeys and humans provides support for RSFC as a viable tool for addressing cross-species comparisons of functional neuroanatomy.

Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach

Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5-16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA components reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.

Functional Connectivity of the Human Amygdala using Resting State fMRI

The amygdala is composed of structurally and functionally distinct nuclei that contribute to the processing of emotion through interactions with other subcortical and cortical structures. While these circuits have been studied extensively in animals, human neuroimaging investigations of amygdala-based networks have typically considered the amygdala as a single structure, which likely masks contributions of individual amygdala subdivisions. The present study uses resting state functional magnetic resonance imaging (fMRI) to test whether distinct functional connectivity patterns, like those observed in animal studies, can be detected across three amygdala subdivisions: laterobasal, centromedial, and superficial. In a sample of 65 healthy adults, voxelwise regression analyses demonstrated positively-predicted ventral and negatively-predicted dorsal networks associated with the total amygdala, consistent with previous animal and human studies. Investigation of individual amygdala subdivisions revealed distinct differences in connectivity patterns within the amygdala and throughout the brain. Spontaneous activity in the laterobasal subdivision predicted activity in temporal and frontal regions, while activity in the centromedial nuclei predicted activity primarily in striatum. Activity in the superficial subdivision positively predicted activity throughout the limbic lobe. These findings suggest that resting state fMRI can be used to investigate human amygdala networks at a greater level of detail than previously appreciated, allowing for the further advancement of translational models.

Etiologic Subtypes of Attention-Deficit/Hyperactivity Disorder: Brain Imaging, Molecular Genetic and Environmental Factors and the Dopamine Hypothesis

Multiple theories of Attention-Deficit/Hyper- activity Disorder (ADHD) have been proposed, but one that has stood the test of time is the dopamine deficit theory. We review the narrow literature from recent brain imaging and molecular genetic studies that has improved our understanding of the role of dopamine in manifestation of symptoms of ADHD, performance deficits on neuropsychological tasks, and response to stimulant medication that constitutes the most common treatment of this disorder. First, we consider evidence of the presence of dopamine deficits based on the recent literature that (1) confirms abnormalities in dopamine-modulated frontal-striatal circuits, reflected by size (smaller-than-average components) and function (hypoactivation); (2) clarifies the agonist effects of stimulant medication on dopaminergic mechanisms at the synaptic and circuit level of analysis; and (3) challenges the most-widely accepted ADHD-related neural abnormality in the dopamine system (higher-than-normal dopamine transporter [DAT] density). Second, we discuss possible genetic etiologies of dopamine deficits based on recent molecular genetic literature, including (1) multiple replications that confirm the association of ADHD with candidate genes related to the dopamine receptor D4 (DRD4) and the DAT; (2) replication of differences in performance of neuropsychological tasks as a function of the DRD4 genotype; and (3) multiple genome-wide linkage scans that demonstrate the limitations of this method when applied to complex disorders but implicate additional genes that may contribute to the genetic basis of ADHD. Third, we review possible environmental etiologies of dopamine deficits based on recent studies of (1) toxic substances that may affect the dopamine system in early development and contribute substantially to the etiology of ADHD; (2) fetal adaptations in dopamine systems in response to stress that may alter early development with lasting effects, as proposed by the developmental origins of health and disease hypothesis; and (3) gene-environment interactions that may moderate selective damage or adaptation of dopamine neurons. Based on these reviews, we identify critical issues about etiologic subtypes of ADHD that may involve dopamine, discuss methods that could be used to address these issues, and review old and new theories that may direct research in this area in the future.

Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies.

OBJECTIVE The authors performed a comprehensive meta-analysis of task-based functional MRI studies of attention deficit hyperactivity disorder (ADHD). METHOD The authors searched PubMed, Ovid, EMBASE, Web of Science, ERIC, CINAHAL, and NeuroSynth for studies published through June 30, 2011. Significant differences in brain region activation between individuals with ADHD and comparison subjects were detected using activation likelihood estimation meta-analysis. Dysfunctional regions in ADHD were related to seven reference neuronal systems. The authors performed a set of meta-analyses focused on age groups (children and adults), clinical characteristics (history of stimulant treatment and presence of psychiatric comorbidities), and specific neuropsychological tasks (inhibition, working memory, and vigilance/attention). RESULTS Fifty-five studies were included (39 for children and 16 for adults). In children, hypoactivation in ADHD relative to comparison subjects was observed mostly in systems involved in executive function (frontoparietal network) and attention (ventral attentional network). Significant hyperactivation in ADHD relative to comparison subjects was observed predominantly in the default, ventral attention, and somatomotor networks. In adults, ADHD-related hypoactivation was predominant in the frontoparietal system, while ADHD-related hyperactivation was present in the visual, dorsal attention, and default networks. Significant ADHD-related dysfunction largely reflected task features and was detected even in the absence of comorbid mental disorders or a history of stimulant treatment. CONCLUSIONS A growing literature provides evidence of ADHD-related dysfunction in multiple neuronal systems involved in higher-level cognitive functions but also in sensorimotor processes, including the visual system, and in the default network. This meta-analytic evidence extends early models of ADHD pathophysiology that were focused on prefrontal-striatal circuits.

Toward a Pathophysiology of Attention-Deficit/Hyperactivint Disorder:

Converging insights into attention-deficit/hyperactivity disorder (ADHD) support the notion that ADHD is best characterized behaviorally as a disorder of self-regulation or executive functioning. Anatomic neuroimaging studies suggest that the relevant regulatory circuits include the prefrontal cortex and the basal ganglia, which are modulated by dopaminergic innervation from the midbrain and by stimulant medications. The emerging model proposed in this review encompasses a developmental perspective into this common condition.

Varieties of Attention-Deficit/Hyperactivity Disorder-Related Intra-Individual Variability

Intra-individual variability in behavior and functioning is ubiquitous among children with attention-deficit/hyperactivity disorder (ADHD), but it has not been systematically examined or integrated within causal models. This article seeks to provide a conceptual, methodologic, and analytic framework as a foundation for future research. We first identify five key research questions and methodologic issues. For illustration, we examine the periodic structure of Eriksen Flanker task reaction time (RT) data obtained from 24 boys with ADHD and 18 age-matched comparison boys. Reaction time variability in ADHD differed quantitatively from control subjects, particularly at a modal frequency around .05 Hz (cycle length approximately 20 sec). These oscillations in RT were unaffected by double-blind placebo and were suppressed by double-blind methylphenidate. Together with converging lines of basic and clinical evidence, these secondary data analyses support the speculative hypothesis that the increased power of multisecond oscillations in ADHD RT data, and by inference, in attentional performance, represents a catecholaminergic deficit in the ability to appropriately modulate such oscillations in neuronal activity. These results highlight the importance of retaining time-series data and quantitatively examining intra-subject measures of variability as a putative endophenotype for ADHD.