Deanna M. Barch

YNIMG Journal 2024 Journal Article

Shared and unique heritability of hippocampal subregion volumes in children and adults

• Jacob G. Pine
• Arpana Agrawal
• Ryan Bogdan
• Sridhar Kandala
• Shelly Cooper
• Deanna M. Barch

Behavioral genetic analyses have not demonstrated robust, unique, genetic correlates of hippocampal subregion volume. Genetic differentiation of hippocampal longitudinal axis subregion volume has not yet been investigated in population-based samples, although this has been demonstrated in rodent and post-mortem human tissue work. The following study is the first population-based investigation of genetic factors that contribute to gray matter volume along the hippocampal longitudinal axis. Twin-based biometric analyses demonstrated that longitudinal axis subregions are associated with significant, unique, genetic variance, and that longitudinal axis subregions are also associated with significant shared, hippocampus-general, genetic factors. Our study's findings suggest that genetic differences in hippocampal longitudinal axis structure can be detected in individual differences in gray matter volume in population-level research designs.

YNIMG Journal 2022 Journal Article

Accuracy and reliability of diffusion imaging models

• Nicole A. Seider
• Babatunde Adeyemo
• Ryland Miller
• Dillan J. Newbold
• Jacqueline M. Hampton
• Kristen M. Scheidter
• Jerrel Rutlin
• Timothy O. Laumann

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.

YNIMG Journal 2022 Journal Article

Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

• Sydney Kaplan
• Dominique Meyer
• Oscar Miranda-Dominguez
• Anders Perrone
• Eric Earl
• Dimitrios Alexopoulos
• Deanna M. Barch
• Trevor K.M. Day

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., "scrubbing") and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

YNIMG Journal 2022 Journal Article

Reliability and stability challenges in ABCD task fMRI data

• James T. Kennedy
• Michael P. Harms
• Ozlem Korucuoglu
• Serguei V. Astafiev
• Deanna M. Barch
• Wesley K. Thompson
• James M. Bjork
• Andrey P. Anokhin

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the 'poor' cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still 'poor') than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

YNIMG Journal 2022 Journal Article

Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI

• Sydney Kaplan
• Anders Perrone
• Dimitrios Alexopoulos
• Jeanette K. Kenley
• Deanna M. Barch
• Claudia Buss
• Jed T. Elison
• Alice M. Graham

T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.

YNIMG Journal 2021 Journal Article

Brain network mechanisms of visual shape completion

• Brian P. Keane
• Deanna M. Barch
• Ravi D. Mill
• Steven M. Silverstein
• Bart Krekelberg
• Michael W. Cole

Visual shape completion recovers object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process's underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate patterns were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 36 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across 95% of observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcel's task activation difference could be modeled via its resting-state connections with the remaining parcels (r=. 62, p<10−9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modelling task activation differences in the secondary visual network. Dorsal attention and frontoparietal connections could also model activations in the remaining networks. Taken together, these results suggest that shape completion relies upon a sparsely distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks.

JBHI Journal 2021 Journal Article

Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort

• Ayse S. Cakmak
• Erick A. Perez Alday
• Giulia Da Poian
• Ali Bahrami Rad
• Thomas J. Metzler
• Thomas C. Neylan
• Stacey L. House
• Francesca L. Beaudoin

Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. Approach: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. Results: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0. 70. The ED survey-based model achieved an AUC of 0. 77, and the fusion of research watch and ED survey metrics improved the AUC to 0. 79. Significance: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.

YNIMG Journal 2021 Journal Article

Meaningful associations in the adolescent brain cognitive development study

• Anthony Steven Dick
• Daniel A. Lopez
• Ashley L. Watts
• Steven Heeringa
• Chase Reuter
• Hauke Bartsch
• Chun Chieh Fan
• David N. Kennedy

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

YNIMG Journal 2021 Journal Article

The Human Connectome Project: A retrospective

• Jennifer Stine Elam
• Matthew F. Glasser
• Michael P. Harms
• Stamatios N. Sotiropoulos
• Jesper L.R. Andersson
• Gregory C. Burgess
• Sandra W. Curtiss
• Robert Oostenveld

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

YNIMG Journal 2020 Journal Article

Correction of respiratory artifacts in MRI head motion estimates

• Damien A. Fair
• Oscar Miranda-Dominguez
• Abraham Z. Snyder
• Anders Perrone
• Eric A. Earl
• Andrew N. Van
• Jonathan M. Koller
• Eric Feczko

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

YNICL Journal 2020 Journal Article

Depressogenic self-schemas are associated with smaller regional grey matter volume in never-depressed preadolescents

• Pan Liu
• Matthew R.J. Vandemeer
• Marc F. Joanisse
• Deanna M. Barch
• David J.A. Dozois
• Elizabeth P. Hayden

Self-referential processing (i.e., self-schemas that guide processing of self-descriptive information) emerges early in youth, with deeper encoding of negative self-descriptors and/or shallower encoding of positive self-descriptors causally linked to depression. However, the relationship between depressogenic self-schemas and brain structure is unclear. We investigated associations between self-schemas and regional grey matter volume (GMV) in 84 never-depressed preadolescents oversampled for depression risk based on maternal depression history. Self-schemas were assessed using a Self-Referent Encoding Task (SRET) and regional GMV was indexed via voxel-based morphometry analysis of structural magnetic resonance imaging data. Youths' positive self-schemas were associated with greater regional GMV within the ventrolateral prefrontal cortex (vlPFC) and posterior cingulate cortex (PCC), while negative self-schemas were associated with smaller regional GMV within vlPFC and PCC, areas important to emotion regulation and self-referential processing. These associations remained significant after controlling for youths' concurrent depressive symptoms. Exploratory mediation analysis suggested that adolescents' depressogenic self-schemas may mediate associations between GMV and depressive symptoms. Our findings suggest that the observed GMV variations within vlPFC and PCC may serve as neurobiological markers of depressogenic self-schemas during preadolescence.

YNICL Journal 2020 Journal Article

Orbitofrontal cortex grey matter volume is related to children’s depressive symptoms

• Matthew R.J. Vandermeer
• Pan Liu
• Ola Mohamed Ali
• Andrew R. Daoust
• Marc F. Joanisse
• Deanna M. Barch
• Elizabeth P. Hayden

Adults with a history of depression show distinct patterns of grey matter volume (GMV) in frontal cortical (e.g., prefrontal cortex, orbitofrontal cortex) and limbic (e.g., anterior cingulate, amygdala, hippocampus, dorsal striatum) structures, regions relevant to the processing and regulation of reward, which is impaired in the context of depression. However, it is unclear whether these GMV associations with depression precede depressive disorder onset or whether GMV is related to early emerging symptoms or familial depression. To address these questions, we used voxel-based morphometry (VBM) to examine GMV in 85 community-dwelling children (M = 11.12 years, SD = 0.63 years) screened for current and lifetime depression. Associations between children's depressive symptoms (self- and mother-report of children's symptoms), children's maternal depression history, and GMV were examined. Although maternal depression history was unrelated to children's GMV, child GMV in the orbitofrontal cortex (OFC) was negatively related to children's self-reported depressive symptoms, using both a priori ROI and whole-brain analyses. Moderated regression analyses indicated that girls' GMV was negatively related to girls' depressive symptoms (as indexed by both self- and mother-report of girls' symptoms), whereas boys' symptoms were positively related to GMV. Our findings suggest that brain morphology in the OFC, a region with functional roles in processes relevant to depressive symptoms (i.e., reward-based learning and reward processing), is associated with early depressive symptoms prior to the development of clinically significant depression.

YNIMG Journal 2020 Journal Article

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

• Caterina Gratton
• Ally Dworetsky
• Rebecca S. Coalson
• Babatunde Adeyemo
• Timothy O. Laumann
• Gagan S. Wig
• Tania S. Kong
• Gabriele Gratton

Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i. e. , ‘motion’ parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (>0. 1 ​Hz, here referred to as ‘HF-motion’), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2. 0–2. 5 ​s) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2. 0 to 2. 5 ​s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants.

YNIMG Journal 2020 Journal Article

Test-retest reliability of fMRI-measured brain activity during decision making under risk

• Ozlem Korucuoglu
• Michael P. Harms
• Serguei V. Astafiev
• James T. Kennedy
• Semyon Golosheykin
• Deanna M. Barch
• Andrey P. Anokhin

Neural correlates of decision making under risk are being increasingly utilized as biomarkers of risk for substance abuse and other psychiatric disorders, treatment outcomes, and brain development. This research relies on the basic assumption that fMRI measures of decision making represent stable, trait-like individual differences. However, reliability needs to be established for each individual construct. Here we assessed long-term test-retest reliability (TRR) of regional brain activations related to decision making under risk using the Balloon Analogue Risk Taking task (BART) and identified regions with good TRRs and familial influences, an important prerequisite for the use of fMRI measures in genetic studies. A secondary goal was to examine the factors potentially affecting fMRI TRRs in one particular risk task, including the magnitude of neural activation, data analytical approaches, different methods of defining boundaries of a region, and participant motion. For the average BOLD response, reliabilities ranged across brain regions from poor to good (ICCs of 0 to 0. 8, with a mean ICC of 0. 17) and highest reliabilities were observed for parietal, occipital, and temporal regions. Among the regions that were of a priori theoretical importance due to their reported associations with decision making, the activation of left anterior insula and right caudate during the decision period showed the highest reliabilities (ICCs of 0. 54 and 0. 63, respectively). Among the regions with highest reliabilities, the right fusiform, right rostral anterior cingulate and left superior parietal regions also showed high familiality as indicated by intrapair monozygotic twin correlations (ranging from 0. 66 to 0. 69). Overall, regions identified by modeling the average BOLD response to a specific event type (rather than its modulation by a parametric regressor), regions including significantly activated vertices (compared to a whole parcel), and regions with greater magnitude of task-related activations showed greater reliabilities. Participant motion had a moderate negative effect on TRR. Regions activated during decision period rather than outcome period of risky decisions showed the greatest TRR and familiality. Regions with reliable activations can be utilized as neural markers of individual differences or endophenotypes in future clinical neuroscience and genetic studies of risk-taking.

YNIMG Journal 2020 Journal Article

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

• Leonardo Tozzi
• Brooke Staveland
• Bailey Holt-Gosselin
• Megan Chesnut
• Sarah E. Chang
• David Choi
• Melissa Shiner
• Hua Wu

Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest (“Emotion”, “Gambling” and “Continuous Performance” tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

YNIMG Journal 2019 Journal Article

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

• Donald J. Hagler
• SeanN. Hatton
• M. Daniela Cornejo
• Carolina Makowski
• Damien A. Fair
• Anthony Steven Dick
• Matthew T. Sutherland
• B.J. Casey

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.

YNIMG Journal 2019 Journal Article

The Lifespan Human Connectome Project in Aging: An overview

• Susan Y. Bookheimer
• David H. Salat
• Melissa Terpstra
• Beau M. Ances
• Deanna M. Barch
• Randy L. Buckner
• Gregory C. Burgess
• Sandra W. Curtiss

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al. , 2018), and the image acquisition protocol common to both studies (Harms et al. , 2018).

YNIMG Journal 2018 Journal Article

Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

• Michael P. Harms
• Leah H. Somerville
• Beau M. Ances
• Jesper Andersson
• Deanna M. Barch
• Matteo Bastiani
• Susan Y. Bookheimer
• Timothy B. Brown

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22–35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

YNIMG Journal 2018 Journal Article

The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds

• Leah H. Somerville
• Susan Y. Bookheimer
• Randy L. Buckner
• Gregory C. Burgess
• Sandra W. Curtiss
• Mirella Dapretto
• Jennifer Stine Elam
• Michael S. Gaffrey

Recent technological and analytical progress in brain imaging has enabled the examination of brain organization and connectivity at unprecedented levels of detail. The Human Connectome Project in Development (HCP-D) is exploiting these tools to chart developmental changes in brain connectivity. When complete, the HCP-D will comprise approximately ∼1750 open access datasets from 1300 + healthy human participants, ages 5–21 years, acquired at four sites across the USA. The participants are from diverse geographical, ethnic, and socioeconomic backgrounds. While most participants are tested once, others take part in a three-wave longitudinal component focused on the pubertal period (ages 9–17 years). Brain imaging sessions are acquired on a 3 T Siemens Prisma platform and include structural, functional (resting state and task-based), diffusion, and perfusion imaging, physiological monitoring, and a battery of cognitive tasks and self-reports. For minors, parents additionally complete a battery of instruments to characterize cognitive and emotional development, and environmental variables relevant to development. Participants provide biological samples of blood, saliva, and hair, enabling assays of pubertal hormones, health markers, and banked DNA samples. This paper outlines the overarching aims of the project, the approach taken to acquire maximally informative data while minimizing participant burden, preliminary analyses, and discussion of the intended uses and limitations of the dataset.

YNIMG Journal 2017 Journal Article

Multimodal neural correlates of cognitive control in the Human Connectome Project

• Dov B. Lerman-Sinkoff
• Jing Sui
• Srinivas Rachakonda
• Sridhar Kandala
• Vince D. Calhoun
• Deanna M. Barch

Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

YNIMG Journal 2016 Journal Article

ConnectomeDB—Sharing human brain connectivity data

• Michael R. Hodge
• William Horton
• Timothy Brown
• Rick Herrick
• Timothy Olsen
• Michael E. Hileman
• Michael McKay
• Kevin A. Archie

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3.

YNICL Journal 2016 Journal Article

Network community structure alterations in adult schizophrenia: identification and localization of alterations

• Dov B. Lerman-Sinkoff
• Deanna M. Barch

A growing body of literature suggests functional connectivity alterations in schizophrenia. While findings have been mixed, evidence points towards a complex pattern of hyper-connectivity and hypo-connectivity. This altered connectivity can be represented and analyzed using the mathematical frameworks provided by graph and information theory to represent functional connectivity data as graphs comprised of nodes and edges linking the nodes. One analytic technique in this framework is the determination and analysis of network community structure, which is the grouping of nodes into linked communities or modules. This data-driven technique finds a best-fit structure such that nodes in a given community have greater connectivity with nodes in their community than with nodes in other communities. These community structure representations have been found to recapitulate known neural-systems in healthy individuals, have been used to identify novel functional systems, and have identified and localized community structure alterations in a childhood onset schizophrenia cohort. In the present study, we sought to determine whether community structure alterations were present in an adult onset schizophrenia cohort while stringently controlling for sources of imaging artifacts. Group level average graphs in healthy controls and individuals with schizophrenia exhibited visually similar network community structures and high amounts of normalized mutual information (NMI). However, testing of individual subject community structures identified small but significant alterations in community structure with alterations being driven by changes in node community membership in the somatosensory, auditory, default mode, salience, and subcortical networks.

YNICL Journal 2016 Journal Article

Subcortical neuromorphometry in schizophrenia spectrum and bipolar disorders

• Daniel Mamah
• Kathryn I. Alpert
• Deanna M. Barch
• John G. Csernansky
• Lei Wang

BACKGROUND: Disorders within the schizophrenia spectrum genetically overlap with bipolar disorder, yet questions remain about shared biological phenotypes. Investigation of brain structure in disease has been enhanced by developments in shape analysis methods that can identify subtle regional surface deformations. Our study aimed to identify brain structure surface deformations that were common across related psychiatric disorders, and characterize differences. METHODS: Using the automated FreeSurfer-initiated Large Deformation Diffeomorphic Metric Mapping, we examined volumes and shapes of seven brain structures: hippocampus, amygdala, caudate, nucleus accumbens, putamen, globus pallidus and thalamus. We compared findings in controls (CON; n = 40), and those with schizophrenia (SCZ; n = 52), schizotypal personality disorder (STP; n = 12), psychotic bipolar disorder (P-BP; n = 49) and nonpsychotic bipolar disorder (N-BP; n = 24), aged 15-35. Relationships between morphometric measures and positive, disorganized and negative symptoms were also investigated. RESULTS: Inward deformation was present in the posterior thalamus in SCZ, P-BP and N-BP; and in the subiculum of the hippocampus in SCZ and STP. Most brain structures however showed unique shape deformations across groups. Correcting for intracranial size resulted in volumetric group differences for caudate (p < 0.001), putamen (p < 0.01) and globus pallidus (p < 0.001). Shape analysis showed dispersed patterns of expansion on the basal ganglia in SCZ. Significant clinical relationships with hippocampal, amygdalar and thalamic volumes were observed. CONCLUSIONS: Few similarities in surface deformation patterns were seen across groups, which may reflect differing neuropathologies. Posterior thalamic contraction in SCZ and BP suggest common genetic or environmental antecedents. Surface deformities in SCZ basal ganglia may have been due to antipsychotic drug effects.

YNIMG Journal 2015 Journal Article

Decomposition of brain diffusion imaging data uncovers latent schizophrenias with distinct patterns of white matter anisotropy

• Javier Arnedo
• Daniel Mamah
• David A. Baranger
• Michael P. Harms
• Deanna M. Barch
• Dragan M. Svrakic
• Gabriel A. de Erausquin
• C. Robert Cloninger

Fractional anisotropy (FA) analysis of diffusion tensor-images (DTI) has yielded inconsistent abnormalities in schizophrenia (SZ). Inconsistencies may arise from averaging heterogeneous groups of patients. Here we investigate whether SZ is a heterogeneous group of disorders distinguished by distinct patterns of FA reductions. We developed a Generalized Factorization Method (GFM) to identify biclusters (i. e. , subsets of subjects associated with a subset of particular characteristics, such as low FA in specific regions). GFM appropriately assembles a collection of unsupervised techniques with Non-negative Matrix Factorization to generate biclusters, rather than averaging across all subjects and all their characteristics. DTI tract-based spatial statistics images, which output is the locally maximal FA projected onto the group white matter skeleton, were analyzed in 47 SZ and 36 healthy subjects, identifying 8 biclusters. The mean FA of the voxels of each bicluster was significantly different from those of other SZ subjects or 36 healthy controls. The eight biclusters were organized into four more general patterns of low FA in specific regions: 1) genu of corpus callosum (GCC), 2) fornix (FX)+external capsule (EC), 3) splenium of CC (SCC)+retrolenticular limb (RLIC)+posterior limb (PLIC) of the internal capsule, and 4) anterior limb of the internal capsule. These patterns were significantly associated with particular clinical features: Pattern 1 (GCC) with bizarre behavior, pattern 2 (FX+EC) with prominent delusions, and pattern 3 (SCC+RLIC+PLIC) with negative symptoms including disorganized speech. The uncovered patterns suggest that SZ is a heterogeneous group of disorders that can be distinguished by different patterns of FA reductions associated with distinct clinical features.

YNIMG Journal 2015 Journal Article

HPA axis genetic variation, pubertal status, and sex interact to predict amygdala and hippocampus responses to negative emotional faces in school-age children

• David Pagliaccio
• Joan L. Luby
• Ryan Bogdan
• Arpana Agrawal
• Michael S. Gaffrey
• Andrew C. Belden
• Kelly N. Botteron
• Michael P. Harms

Accumulating evidence suggests a role for stress exposure, particularly during early life, and for variation in genes involved in stress response pathways in neural responsivity to emotional stimuli. Understanding how individual differences in these factors predict differences in emotional responsivity may be important for understanding both normative emotional development and for understanding the mechanisms underlying internalizing disorders, like anxiety and depression, that have often been related to increased amygdala and hippocampus responses to negatively valenced emotional stimuli. The present study examined whether stress exposure and genetic profile scores (10 single nucleotide polymorphisms within four hypothalamic–pituitary–adrenal axis genes: CRHR1, NR3C2, NR3C1, and FKBP5) predict individual differences in amygdala and hippocampus responses to fearful vs. neutral faces in school-age children (7–12year olds; N =107). Experience of more stressful and traumatic life events predicted greater left amygdala responses to negative emotional stimuli. Genetic profile scores interacted with sex and pubertal status to predict amygdala and hippocampus responses. Specifically, genetic profile scores were a stronger predictor of amygdala and hippocampus responses among pubertal vs. prepubertal children where they positively predicted responses to fearful faces among pubertal girls and positively predicted responses to neutral faces among pubertal boys. The current results suggest that genetic and environmental stress-related factors may be important in normative individual differences in responsivity to negative emotional stimuli, a potential mechanism underlying internalizing disorders. Further, sex and pubertal development may be key moderators of the effects of stress-system genetic variation on amygdala and hippocampus responsivity, potentially relating to sex differences in stress-related psychopathology.

YNICL Journal 2013 Journal Article

Conflict and error processing in an extended cingulo-opercular and cerebellar network in schizophrenia

• Karla E. Becerril
• Deanna M. Barch

The loss of cognitive control is a prominent feature of schizophrenia. Relevant for adaptive control, individuals with schizophrenia often show impairments in their ability to monitor their ongoing behavior, and to adjust their responses based on advance information or feedback. By conducting a systematic examination of the behavioral adjustments after error and conflict and of activity within and between brain regions sensitive to the need to increase control (i.e. error commission, conflict presentation) in individuals with schizophrenia (n = 38) compared to healthy controls, we aimed to 1) shed light on the role of diverse brain regions previously associated with adaptive cognitive control, and 2) contribute to our understanding of the nature of the cognitive deficits present in individuals with schizophrenia. Our results show that error- and conflict-related behavioral adjustments are relatively intact during the performance of a change-signal task. Similarly, individuals with schizophrenia demonstrated intact error- and conflict-related effects in the dorsal anterior cingulate cortex, as well as in a number of other key regions including the bilateral anterior prefrontal cortex (PFC), bilateral insula, right inferior parietal lobule during error processing, and bilateral inferior parietal lobule and thalamus, right anterior PFC, left insula, and left lateral and inferior cerebellum during conflict processing. Given that a critical characteristic of our experimental design was the use of tasks that explicitly provide information about errors and conflict, we interpret our results as suggesting that the error- and conflict-detection systems are still somewhat functional in individuals with schizophrenia, but that a compromise in the ability to represent task relevant information that allow for the generation of an error representation may lead to the alterations in error- and conflict-processing documented in the schizophrenia literature.

YNIMG Journal 2013 Journal Article

Function in the human connectome: Task-fMRI and individual differences in behavior

• Deanna M. Barch
• Gregory C. Burgess
• Michael P. Harms
• Steven E. Petersen
• Bradley L. Schlaggar
• Maurizio Corbetta
• Matthew F. Glasser
• Sandra Curtiss

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.

YNIMG Journal 2013 Journal Article

Human Connectome Project informatics: Quality control, database services, and data visualization

• Daniel S. Marcus
• Michael P. Harms
• Abraham Z. Snyder
• Mark Jenkinson
• J. Anthony Wilson
• Matthew F. Glasser
• Deanna M. Barch
• Kevin A. Archie

The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.

YNIMG Journal 2013 Journal Article

The WU-Minn Human Connectome Project: An overview

• David C. Van Essen
• Stephen M. Smith
• Deanna M. Barch
• Timothy E.J. Behrens
• Essa Yacoub
• Kamil Ugurbil

The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.

YNIMG Journal 2012 Journal Article

Automated landmark identification for human cortical surface-based registration

• Alan Anticevic
• Grega Repovs
• Donna L. Dierker
• John W. Harwell
• Timothy S. Coalson
• Deanna M. Barch
• David C. Van Essen

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas.

YNIMG Journal 2012 Journal Article

Cognitive training-related changes in hippocampal activity associated with recollection in older adults

• Brenda A. Kirchhoff
• Benjamin A. Anderson
• Staci E. Smith
• Deanna M. Barch
• Larry L. Jacoby

Impairments in the ability to recollect specific details of personally experienced events are one of the main cognitive changes associated with aging. Cognitive training can improve older adults' recollection. However, little is currently known regarding the neural correlates of these training-related changes in recollection. Prior research suggests that the hippocampus plays a central role in supporting recollection in young and older adults, and that age-related changes in hippocampal function may lead to age-related changes in recollection. The present study investigated whether cognitive training-related increases in older adults' recollection are associated with changes in their hippocampal activity during memory retrieval. Older adults' hippocampal activity during retrieval was examined before and after they were trained to use semantic encoding strategies to intentionally encode words. Training-related changes in recollection were positively correlated with training-related changes in activity for old words in the hippocampus bilaterally. Positive correlations were also found between training-related changes in activity in prefrontal and left lateral temporal regions associated with self-initiated semantic strategy use during encoding and training-related changes in right hippocampal activity associated with recollection during retrieval. These results suggest that cognitive training-related improvements in older adults' recollection can be supported by changes in their hippocampal activity during retrieval. They also suggest that age differences in cognitive processes engaged during encoding are a significant contributor to age differences in recollection during retrieval.

YNIMG Journal 2011 Journal Article

Error processing network dynamics in schizophrenia

• Karla E. Becerril
• Grega Repovs
• Deanna M. Barch

Current theories of cognitive dysfunction in schizophrenia emphasize an impairment in the ability of individuals suffering from this disorder to monitor their own performance, and adjust their behavior to changing demands. Detecting an error in performance is a critical component of evaluative functions that allow the flexible adjustment of behavior to optimize outcomes. The dorsal anterior cingulate cortex (dACC) has been repeatedly implicated in error-detection and implementation of error-based behavioral adjustments. However, accurate error-detection and subsequent behavioral adjustments are unlikely to rely on a single brain region. Recent research demonstrates that regions in the anterior insula, inferior parietal lobule, anterior prefrontal cortex, thalamus, and cerebellum also show robust error-related activity, and integrate into a functional network. Despite the relevance of examining brain activity related to the processing of error information and supporting behavioral adjustments in terms of a distributed network, the contribution of regions outside the dACC to error processing remains poorly understood. To address this question, we used functional magnetic resonance imaging to examine error-related responses in 37 individuals with schizophrenia and 32 healthy controls in regions identified in the basic science literature as being involved in error processing, and determined whether their activity was related to behavioral adjustments. Our imaging results support previous findings showing that regions outside the dACC are sensitive to error commission, and demonstrated that abnormalities in brain responses to errors among individuals with schizophrenia extend beyond the dACC to almost all of the regions involved in error-related processing in controls. However, error related responses in the dACC were most predictive of behavioral adjustments in both groups. Moreover, the integration of this network of regions differed between groups, with the cerebellar regions and the dACC less connected to the network in individuals with schizophrenia compared to controls. Our findings demonstrate a blunted response to error commission in the dACC that is associated with reduced error-related behavioral adjustments in individuals with schizophrenia. This result supports the hypothesis that a failure to respond appropriately to errors in individuals with schizophrenia is linked to alterations in dACC function leading to a compromise in the implementation of cognitive control. Our findings highlight the importance of examining brain activity related to the processing of error information and supporting error-related behavioral adjustments in terms of a distributed network.

YNIMG Journal 2011 Journal Article

Impaired error-likelihood prediction in medial prefrontal cortex in schizophrenia

• Adam Krawitz
• Todd S. Braver
• Deanna M. Barch
• Joshua W. Brown

The cognitive impairment in individuals with schizophrenia includes deficits of working memory in dorsolateral prefrontal cortex and deficits of performance monitoring in medial prefrontal cortex (MPFC). Recent work suggests a more general role for MPFC in predicting the outcome of actions and then evaluating those predictions. Here we investigate, in individuals with schizophrenia, two specific effects associated with this role: the error likelihood effect (occurring on trials with correct performance, but features that predict a high probability of errors), and the error unexpectedness effect (occurring on trials with an error, but features that predict errors are of low probability). In a rapid event-related fMRI design with a modified version of the change-signal task, a cue incidentally predicting error likelihood was encoded into working memory by participants in order to perform a secondary delayed match-to-sample task. There were four key findings: (1) individuals with schizophrenia exhibited poorer working memory performance and reduced error signals in MPFC; (2) even in control and schizophrenia subgroups matched on working memory performance, the schizophrenia subgroup showed a deficit in error-likelihood prediction in MPFC at the time of the predictive cue; (3) the schizophrenia subgroup also showed a deficit in evaluative error-unexpectedness activity when errors were committed; and (4) a mediation analysis indicated that error-likelihood predictions successfully explained error-unexpectedness evaluations in both controls and patients. Collectively, these findings suggest that individuals with schizophrenia have a disturbance in the evaluation of outcomes that is the result of a primary deficit in the prediction of error likelihood in MPFC.

YNIMG Journal 2010 Journal Article

When less is more: TPJ and default network deactivation during encoding predicts working memory performance

• Alan Anticevic
• Grega Repovs
• Gordon L. Shulman
• Deanna M. Barch

Previous work has shown that temporo-parietal junction (TPJ), part of a ventral attention network for stimulus-driven reorienting, deactivates during effortful cognitive engagement, along with the default mode network (DMN). TPJ deactivation has been reported both during working memory (WM) and rapid visual search, ostensibly to prevent reorienting to irrelevant objects. We tested whether the magnitude of this deactivation during WM encoding is predictive of subsequent WM performance. Using slow event-related fMRI and a delayed WM task in which distracter stimuli were presented during the maintenance phase, we found that greater TPJ and DMN deactivation during the encoding phase predicted better WM performance. TPJ and DMN, however, also showed several functional dissociations: (1) TPJ exhibited a different task-evoked pattern than DMN, responding to distracters sharing task-relevant features, but not to other types of distracters; and (2) TPJ showed strong functional connectivity with the DMN at encoding but not during distracter presentation. These results provide further evidence for the functional importance of TPJ suppression and indicate that TPJ and DMN deactivation is especially critical during WM trace formation. In addition, the functional connectivity results suggest that TPJ, while not part of the DMN during the resting state, may flexibly “couple” with this network depending on task demands.

YNIMG Journal 2008 Journal Article

Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia

• Alan Anticevic
• Donna L. Dierker
• Sarah K. Gillespie
• Grega Repovs
• John G. Csernansky
• David C. Van Essen
• Deanna M. Barch

A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.

YNIMG Journal 2008 Journal Article

Symmetric abnormalities in sulcal patterning in schizophrenia

• John G. Csernansky
• Sarah K. Gillespie
• Donna L. Dierker
• Alan Anticevic
• Lei Wang
• Deanna M. Barch
• David C. Van Essen

To compare the morphology of the cerebral cortex and its characteristic pattern of gyri and sulci in individuals with and without schizophrenia, T1-weighted magnetic resonance scans were collected, along with clinical and cognitive information, from 33 individuals with schizophrenia and 30 healthy individuals group-matched for age, gender, race and parental socioeconomic status. Sulcal depth was measured across the entire cerebral cortex by reconstructing surfaces of cortical mid-thickness (layer 4) in each hemisphere and registering them to the human PALS cortical atlas. Group differences in sulcal depth were tested using methods for cluster size analysis and interhemispheric symmetry analysis. A significant group difference was found bilaterally in the parietal operculum, where the average sulcal depth was shallower in individuals with schizophrenia. In addition, group differences in sulcal depth showed significant bilateral symmetry across much of the occipital, parietal, and temporal cortices. In individuals with schizophrenia, sulcal depth in the left hemisphere was correlated with the severity of impaired performance on tests of working memory and executive function.

YNIMG Journal 2007 Journal Article

Performance on an episodic encoding task yields further insight into functional brain development

• Tara McAuley
• Shefali Brahmbhatt
• Deanna M. Barch

To further characterize changes in functional brain development that are associated with the emergence of cognitive control, participants 14 to 28 years of age were scanned while performing an episodic encoding task with a levels-of-processing manipulation. Using data from the 12 youngest and oldest participants (endpoint groups), 18 regions were identified that showed group differences in task-related activity as a function of processing depth. One region, located in left inferior frontal gyrus, showed enhanced activity in deep relative to shallow encoding that was larger in magnitude for the older group. Seventeen regions showed enhanced activity in shallow relative to deep encoding that was larger in magnitude for the youngest group. These regions were distributed across a broad network that included both cortical and subcortical areas. Regression analyses using the entire sample showed that age made a significant contribution to the difference in beta weights between deep and shallow encoding for 17 of the 18 identified regions in the direction predicted by the endpoint analysis. We conclude that the patterns of brain activation associated with deep and shallow encoding differ between adolescents and young adults in a manner that is consistent with the interactive specialization account of functional brain development.

YNIMG Journal 2006 Journal Article

Sex influences on material-sensitive functional lateralization in working and episodic memory: Men and women are not all that different

• Kristen M. Haut
• Deanna M. Barch

Research investigating the effects of sex on the lateralization of language functions has produced mixed results to date, with some studies finding sex differences and others not [Shaywitz, B. A. , Shaywitz, S. E. , Pugh, K. R. , Constable, R. T. , Skudlarski, P. , Fulbright, R. K. , Bronen, R. A. , Fletcher, J. M. , Shankweiler, D. P. , Katz, L. , et al. , 1995. Sex differences in the functional organization of the brain for language. Nature 373 607–609. ; Frost, J. A. , Binder, J. R. , Springer, J. A. , Hammeke, T. A. , Bellgowan, P. S. , Rao, S. M. , Cox, R. W. , 1999. Language processing is strongly left lateralized in both sexes. Evidence from functional MRI. Brain 122 (Pt 2) 199–208. ]. Further, few studies have evaluated how any such sex effects extend to tasks involving cognitive functions that may utilize language processes such as working and episodic memory. This study examined sex difference in material-sensitive functional activation (using fMRI) in working memory and episodic memory that included either words and faces. We performed these analyses on two large groups of healthy subjects with the goal of attempting to replicate results across two independent data sets. The results indicated that both males and females showed strong and consistent evidence for material-sensitive lateralization for both working and episodic memory, such that word tasks resulted in greater left-sided activation and face tasks resulted in greater right-sided activation. Further, few of the sex differences in regions showing material specificity effects in at least one gender replicated across studies, providing little evidence for any differences in lateralization patterns between the sexes. In conclusion, our data suggest that males and females show a similar pattern of lateralized activation to material type during working memory and recognition tasks.

YNIMG Journal 2005 Journal Article

The BOLD onset transient: identification of novel functional differences in schizophrenia

• Michael D. Fox
• Abraham Z. Snyder
• Mark P. McAvoy
• Deanna M. Barch
• Marcus E. Raichle

Blood oxygen level dependent (BOLD) signals characteristically exhibit an overshoot (transient signal increase) at the beginning of fMRI task blocks. This onset transient has often been overlooked as an independent measure of neuronal activity, but it may represent unique functional processes. We examined onset transient responses in normal subjects and individuals with schizophrenia performing three cognitive tasks. These analyses revealed a regionally specific and task specific attenuation of the onset transient in individuals with schizophrenia during performance of a working memory task. Furthermore, this attenuation was often not accompanied by a corresponding population difference in the sustained response, and is missed through conventional fMRI analysis techniques. Relevance of these findings to both an interpretation of the onset transient and the pathology of schizophrenia are discussed.

YNIMG Journal 2005 Journal Article

Transient BOLD responses at block transitions

• Michael D. Fox
• Abraham Z. Snyder
• Deanna M. Barch
• Debra A. Gusnard
• Marcus E. Raichle

Block-design fMRI responses include sustained components present for the duration of each task block as well as transient components at the beginning and end of each block. Almost all prior block-design fMRI studies have focused on the sustained response components while the transient responses at block transitions have been largely ignored. These transients, therefore, remain poorly characterized. We here present a systematic study of block-transition transient responses obtained using four widely divergent tasks. We characterize transient response topography and examine the extent to which these responses vary across different tasks and between block onset and offset. Our analysis reveals that certain regions show transient responses regardless of task or transition type. However, our analysis also shows that specific task state transitions give rise to transient responses with unique spatial profiles. Relevance of the current findings to studies of exogenous attention, task shifting, and the BOLD overshoot is discussed.

YNIMG Journal 2001 Journal Article

Direct Comparison of Prefrontal Cortex Regions Engaged by Working and Long-Term Memory Tasks

• Todd S. Braver
• Deanna M. Barch
• William M. Kelley
• Randy L. Buckner
• Neal J. Cohen
• Francis M. Miezin
• Abraham Z. Snyder
• John M. Ollinger

Neuroimaging studies have suggested the involvement of ventrolateral, dorsolateral, and frontopolar prefrontal cortex (PFC) regions in both working (WM) and long-term memory (LTM). The current study used functional magnetic resonance imaging (fMRI) to directly compare whether these PFC regions show selective activation associated with one memory domain. In a within-subjects design, subjects performed the n-back WM task (two-back condition) as well as LTM encoding (intentional memorization) and retrieval (yes–no recognition) tasks. Additionally, each task was performed with two different types of stimulus materials (familiar words, unfamiliar faces) in order to determine the influence of material-type vs task-type. A bilateral region of dorsolateral PFC (DL-PFC; BA 46/9) was found to be selectively activated during the two-back condition, consistent with a hypothesized role for this region in active maintenance and/or manipulation of information in WM. Left frontopolar PFC (FP-PFC) was also found to be selectively engaged during the two-back. Although FP-PFC activity has been previously associated with retrieval from LTM, no frontopolar regions were found to be selectively engaged by retrieval. Finally, lateralized ventrolateral PFC (VL-PFC) regions were found to be selectively engaged by material-type, but uninfluenced by task-type. These results highlight the importance of examining PFC activity across multiple memory domains, both for functionally differentiating PFC regions (e. g. , task-selectivity vs material-selectivity in DL-PFC and VL-PFC) and for testing the applicability of memory domain-specific theories (e. g. , FP-PFC in LTM retrieval).

YNIMG Journal 1999 Journal Article

Overt Verbal Responding during fMRI Scanning: Empirical Investigations of Problems and Potential Solutions

• Deanna M. Barch
• Fred W. Sabb
• Cameron S. Carter
• Todd S. Braver
• Douglas C. Noll
• Jonathan D. Cohen

This paper presents a pair of studies designed to empirically explore the severity of potential artifacts associated with overt verbal responding during fMRI scanning and to examine several different solutions to these artifacts. In Study One, we compared susceptibility artifacts, signal-to-noise ratios, and activation patterns when overt versus covert verbal responses were elicited during fMRI scanning, using both individual and group analyses. The results indicated that different patterns of brain activation were elicited during covert as compared to overt verbal responses. This suggests that covert responses cannot be used as a simple substitute for overt verbal responses. Further, the results suggested that the use of overt verbal responses during fMRI scanning can produce interpretable results if: (1) the primary comparison is between two conditions that both use overt verbal responses, and (2) analyses are conducted on pooled group data rather than individual participant data. In Study Two, we evaluated the feasibility and validity of a method for acquiring participants' overt responses during fMRI scanning. The results indicated that our method was very accurate in acquiring the content of participant's responses. Further, inspection of the responses demonstrated that participants do not always comply with task instructions and highlighted the importance of obtaining behavioral performance measures during fMRI scanning.

YNIMG Journal 1998 Journal Article

Effects of Verbal Responding on Movement in fMRI

• Deanna M. Barch
• Cameron S. Carter
• Fred Sabh
• Douglas C. Noll
• Jonathan D. Cohen