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The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.

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.

The experience of self is unique and pivotal to clinically relevant cognitive and emotional functions. However, well-controlled data on specialized brain regions and functional networks underlying the experience of self remain limited. This functional magnetic resonance imaging study investigated neural activity and connectivity specific to processing one's own face in healthy women by examining neural responses to the pictures of the subjects' own faces in contrast to faces of their own mothers, female friends and strangers during passive viewing, emotional and self-relevance evaluations. The processing of one's own face in comparison to processing of familiar faces revealed significant activity in right anterior insula (AI) and left inferior parietal lobule (IPL), and less activity in right posterior cingulate/precuneus (PCC/PCu) across all tasks. Further, the seed-based correlation analysis of right AI, and left IPL, showed differential functional networks in self and familiar faces contrasts. There were no differences in valence and saliency ratings between self and familiar others. Our preliminary results suggest that the self-experience cued by self-face is processed predominantly by brain regions and related networks that link interoceptive feelings and sense of body ownership to self-awareness and less by regions of higher order functioning such as autobiographical memories., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Major depressive disorder (MDD) has been characterized by excessive default-network activation and connectivity with the subgenual cingulate. These hyper-connectivities are often interpreted as reflecting rumination, where MDDs perseverate on negative, self-referential thoughts. However, the relationship between connectivity and rumination has not been established. Furthermore, previous research has not examined how connectivity with the subgenual cingulate differs when individuals are engaged in a task or not. The purpose of the present study was to examine connectivity of the default network specifically in the subgenual cingulate both on- and off-task, and to examine the relationship between connectivity and rumination. Analyses using a seed-based connectivity approach revealed that MDDs show more neural functional connectivity between the posterior-cingulate cortex and the subgenual-cingulate cortex than healthy individuals during rest periods, but not during task engagement. Importantly, these rest-period connectivities correlated with behavioral measures of rumination and brooding, but not reflection.

Prenatal cocaine exposure (PCE) is associated with attention/arousal dysregulation and possible inefficiencies in some cognitive functions. However, the neurobiological bases of these teratogenic effects have not been well characterized. Because activities in the default mode network (DMN) reflect intrinsic brain functions that are closely associated with arousal regulation and cognition, alterations in the DMN could underlie cognitive effects related to PCE. With resting-state and task activation functional magnetic resonance imaging (fMRI), this study investigated the possible PCE related changes in functional brain connectivity and brain activation in the DMN. In the resting state, the PCE group was found to have stronger functional connectivity in the DMN, as compared to the nonexposed controls. During a working memory task with emotional distracters, the PCE group exhibited less deactivation in the DMN and their fMRI signal was more increased by emotional arousal. These data revealed additional neural effects related to PCE, and consistent with previous findings, indicate that PCE may affect behavior and functioning by increasing baseline arousal and altering the excitatory/inhibitory balancing mechanisms involved in cognitive resource allocation., (Copyright © 2010 Wiley-Liss, Inc.)

This article describes the combination of multivariate Granger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network., (2008 Wiley-Liss, Inc.)

This article introduces the measure of integrated local correlation (ILC) for assessing local coherence in the brain using functional magnetic resonance imaging (fMRI) data and characterizes the measure in terms of reproducibility, the effect of physiological noise, and the dependence on image resolution. The coupling of local neuronal processes influences coherence in a voxel's neighborhood. ILC is defined, for each voxel, as the integration of its spatial correlation function. This integrated measure does not require the specification of a neighborhood and, as demonstrated by experimental data, is effectively independent of image resolution. Respiratory and cardiac fluctuations do not considerably alter the ILC value except in isolated areas in and surrounding large vessels. With resting-state fMRI data, ILC was demonstrated to be tissue-specific, higher in gray matter than white matter, and reproducible across consecutive runs in healthy individuals. Within the gray matter, ILC was found to be higher in the default mode network, particularly the posterior and anterior cingulate cortices. Comparing ILC maps obtained from resting state and continuous motor task data, we observed reduced local coherence in the default mode network during the task. Finally, we compared ILC and regional homogeneity by examining their ability to discriminate between gray and white matters in resting state data and found ILC to be more sensitive., ((c) 2007 Wiley-Liss, Inc.)

In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion.

The thorough characterization of transgenic mouse models of human central nervous system diseases is a necessary step in realizing the full benefit of using animal models to investigate disease processes and potential therapeutics. Because of the labor- and resource-intensive nature of high-resolution imaging, detailed investigation of possible structural or biochemical alterations in brain sections has typically focused on specific regions of interest as determined by the researcher a priori. For example, Parkinson's disease researchers often focus imaging on regions of the brain expected to exhibit pathology such as the substantia nigra and striatum. Because of limitations in acquiring and storing high-resolution imaging data, additional data contained in the specimen is not usually acquired or disseminated/reported to the research community. Here we present a method of imaging large regions of brain at close to the resolution limit of light microscopy using a mosaic imaging technique in conjunction with multiphoton microscopy. These maps are being used to characterize several genetically modified animal models of neurological disease by filling the information "gap" among techniques such as magnetic resonance imaging and electron microscopic analysis.