R. Todd Constable

YNIMG Journal 2022 Journal Article

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

• Kangjoo Lee
• Corey Horien
• David O'Connor
• Bronwen Garand-Sheridan
• Fuyuze Tokoglu
• Dustin Scheinost
• Evelyn M.R. Lake
• R. Todd Constable

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

YNIMG Journal 2022 Journal Article

Inside information: Systematic within-node functional connectivity changes observed across tasks or groups

• Wenjing Luo
• R. Todd Constable

Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .

YNICL Journal 2021 Journal Article

Brain anatomical covariation patterns linked to binge drinking and age at first full drink

• Yihong Zhao
• R. Todd Constable
• Denise Hien
• Tammy Chung
• Marc N. Potenza

Binge drinking and age at first full drink (AFD) of alcohol prior to 21 years (AFD < 21) have been linked to neuroanatomical differences in cortical and subcortical grey matter (GM) volume, cortical thickness, and surface area. Despite the importance of understanding network-level relationships, structural covariation patterns among these morphological measures have yet to be examined in relation to binge drinking and AFD < 21. Here, we used the Joint and Individual Variance Explained (JIVE) method to characterize structural covariation patterns common across and specific to morphological measures in 293 participants (149 individuals with past-12-month binge drinking and 144 healthy controls) from the Human Connectome Project (HCP). An independent dataset (Nathan Kline Institute Rockland Sample; NKI-RS) was used to examine reproducibility/generalizability. We identified a reproducible joint component dominated by structural covariation between GM volume in the brainstem and thalamus proper, and GM volume and surface area in prefrontal cortical regions. Using linear mixed regression models, we found that participants with AFD < 21 showed lower joint component scores in both the HCP (beta = 0.059, p-value = 0.016; Cohen's d = 0.441) and NKI-RS (beta = 0.023, p-value = 0.040, Cohen's d = 0.216) datasets, whereas the individual thickness component associated with binge drinking (p-value = 0.02) and AFD < 21 (p-value < 0.001) in the HCP dataset was not statistically significant in the NKI-RS sample. Our findings were also generalizable to the HCP full sample (n = 880 participants). Taken together, our results show that use of JIVE analysis in high-dimensional, large-scale, psychiatry-related datasets led to discovery of a reproducible cortical and subcortical structural covariation pattern involving brain regions relevant to thalamic-PFC-brainstem neural circuitry which is related to AFD < 21 and suggests a possible extension of existing addiction neurocircuitry in humans.

YNIMG Journal 2021 Journal Article

Resample aggregating improves the generalizability of connectome predictive modeling

• David O'Connor
• Evelyn M.R. Lake
• Dustin Scheinost
• R. Todd Constable

It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.

YNIMG Journal 2021 Journal Article

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

• Wenjing Luo
• Abigail S. Greene
• R. Todd Constable

Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

YNIMG Journal 2020 Journal Article

Cluster failure or power failure? Evaluating sensitivity in cluster-level inference

• Stephanie Noble
• Dustin Scheinost
• R. Todd Constable

Pioneering work in human neuroscience has relied on the ability to map brain function using task-based fMRI, but the empirical validity of these inferential methods is still being characterized. A recent landmark study by Eklund and colleagues showed that popular multiple comparison corrections based on cluster extent suffer from unexpectedly low specificity (i.e., high false positive rate). Yet that study's focus on specificity, while important, is incomplete. The validity of a method depends also on its sensitivity (i.e., true positive rate or power), yet the sensitivity of cluster correction remains poorly understood. Here, we assessed the sensitivity of gold-standard nonparametric cluster correction by resampling real data from five tasks in the Human Connectome Project and comparing results with those from the full "ground truth" datasets (n ​= ​480-493). Critically, we found that sensitivity after correction is lower than may be practical for many fMRI applications. In particular, sensitivity to medium-sized effects (|Cohen's d| ​= ​0.5) was less than 20% across tasks on average, about three times smaller than without any correction. Furthermore, cluster extent correction exhibited a spatial bias in sensitivity that was independent of effect size. In comparison, correction based on the Threshold-Free Cluster Enhancement (TFCE) statistic approximately doubled sensitivity across tasks but increased spatial bias. These results suggest that we have, until now, only measured the tip of the iceberg in the activation-mapping literature due to our goal of limiting the familywise error rate through cluster extent-based inference. There is a need to revise our practices to improve sensitivity; we therefore conclude with a list of modern strategies to boost sensitivity while maintaining respectable specificity in future investigations.

YNIMG Journal 2020 Journal Article

Connectome-based neurofeedback: A pilot study to improve sustained attention

• Dustin Scheinost
• Tiffany W. Hsu
• Emily W. Avery
• Michelle Hampson
• R. Todd Constable
• Marvin M. Chun
• Monica D. Rosenberg

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.

YNIMG Journal 2020 Journal Article

Individualized functional networks reconfigure with cognitive state

• Mehraveh Salehi
• Amin Karbasi
• Daniel S. Barron
• Dustin Scheinost
• R. Todd Constable

There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static. In this work, we use fMRI data obtained across multiple cognitive states (task-evoked and rest conditions) and across multiple subjects, to measure state- and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state. Such reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy. Our findings suggest that if functional networks are to be defined via functional clustering of nodes, then it is essential to consider that such definitions may be fluid and cognitive-state dependent.

YNIMG Journal 2020 Journal Article

There is no single functional atlas even for a single individual: Functional parcel definitions change with task

• Mehraveh Salehi
• Abigail S. Greene
• Amin Karbasi
• Xilin Shen
• Dustin Scheinost
• R. Todd Constable

The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

YNIMG Journal 2019 Journal Article

A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis

• Stephanie Noble
• Dustin Scheinost
• R. Todd Constable

Background Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. Objectives We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. Methods A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. Results From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a “poor” ICC of 0. 29 (95% CI = 0. 23 to 0. 36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. Conclusion This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.

YNIMG Journal 2019 Journal Article

Combining multiple connectomes improves predictive modeling of phenotypic measures

• Siyuan Gao
• Abigail S. Greene
• R. Todd Constable
• Dustin Scheinost

Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks—the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naïve extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.

YNIMG Journal 2019 Journal Article

Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies

• Angus Ho Ching Fong
• Kwangsun Yoo
• Monica D. Rosenberg
• Sheng Zhang
• Chiang-shan R. Li
• Dustin Scheinost
• R. Todd Constable
• Marvin M. Chun

Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i. e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10–60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i. e. less variable, information flow between brain regions.

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

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors

• Kwangsun Yoo
• Monica D. Rosenberg
• Stephanie Noble
• Dustin Scheinost
• R. Todd Constable
• Marvin M. Chun

Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.

YNIMG Journal 2019 Journal Article

Ten simple rules for predictive modeling of individual differences in neuroimaging

• Dustin Scheinost
• Stephanie Noble
• Corey Horien
• Abigail S. Greene
• Evelyn MR. Lake
• Mehraveh Salehi
• Siyuan Gao
• Xilin Shen

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

YNIMG Journal 2019 Journal Article

The individual functional connectome is unique and stable over months to years

• Corey Horien
• Xilin Shen
• Dustin Scheinost
• R. Todd Constable

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1–2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

YNIMG Journal 2018 Journal Article

An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks

• Mehraveh Salehi
• Amin Karbasi
• Xilin Shen
• Dustin Scheinost
• R. Todd Constable

Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of “submodularity” to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i. e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.

YNIMG Journal 2018 Journal Article

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets

• Kwangsun Yoo
• Monica D. Rosenberg
• Wei-Ting Hsu
• Sheng Zhang
• Chiang-shan R. Li
• Dustin Scheinost
• R. Todd Constable
• Marvin M. Chun

Connectome-based predictive modeling (CPM; Finn et al. , 2015; Shen et al. , 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al. , 2015) and sustained attention (Rosenberg et al. , 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al. , 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al. , 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al. , 2014a; Rosenberg et al. , 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al. , in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al. , 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0. 9 for internal validation, and 0. 6 for external validation (all p's < 0. 05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al. , 2016a), it is useful to consider accordance features and PLS regression for CPM.

YNIMG Journal 2018 Journal Article

Considering factors affecting the connectome-based identification process: Comment on Waller et al.

• Corey Horien
• Stephanie Noble
• Emily S. Finn
• Xilin Shen
• Dustin Scheinost
• R. Todd Constable

A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.

YNICL Journal 2018 Journal Article

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants

• Daniel S. Barron
• Mehraveh Salehi
• Michael Browning
• Catherine J. Harmer
• R. Todd Constable
• Eugene Duff

Background: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n = 306) exploring the effect of antidepressant administration on emotional face processing. Methods: We created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies. Results: We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50 to 87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another. Conclusions: We found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development.

YNIMG Journal 2018 Journal Article

Topographic organization of the cerebral cortex and brain cartography

• Simon B. Eickhoff
• R. Todd Constable
• B.T. Thomas Yeo

One of the most specific but also challenging properties of the brain is its topographic organization into distinct modules or cortical areas. In this paper, we first review the concept of topographic organization and its historical development. Next, we provide a critical discussion of the current definition of what constitutes a cortical area, why the concept has been so central to the field of neuroimaging and the challenges that arise from this view. A key aspect in this discussion is the issue of spatial scale and hierarchy in the brain. Focusing on in-vivo brain parcellation as a rapidly expanding field of research, we highlight potential limitations of the classical concept of cortical areas in the context of multi-modal parcellation and propose a revised interpretation of cortical areas building on the concept of neurobiological atoms that may be aggregated into larger units within and across modalities. We conclude by presenting an outlook on the implication of this revised concept for future mapping studies and raise some open questions in the context of brain parcellation.

YNIMG Journal 2017 Journal Article

Can brain state be manipulated to emphasize individual differences in functional connectivity?

• Emily S. Finn
• Dustin Scheinost
• Daniel M. Finn
• Xilin Shen
• Xenophon Papademetris
• R. Todd Constable

While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

YNICL Journal 2017 Journal Article

Cingulate cortex functional connectivity predicts future relapse in alcohol dependent individuals

• Yasmin Zakiniaeiz
• Dustin Scheinost
• Dongju Seo
• Rajita Sinha
• R. Todd Constable

Alcohol dependence is a chronic relapsing illness. Alcohol and stress cues have consistently been shown to increase craving and relapse risk in recovering alcohol dependent (AUD) patients. However, differences in functional connectivity in response to these cues have not been studied using data-driven approaches. Here, voxel-wise connectivity is used in a whole-brain investigation of functional connectivity differences associated with alcohol and stress cues and to examine whether these differences are related to subsequent relapse. In Study 1, 45, 4- to 8-week abstinent, recovering AUD patients underwent functional magnetic resonance imaging during individualized imagery of alcohol, stress, and neutral cues. Relapse measures were collected prospectively for 90 days post-discharge from inpatient treatment. AUD patients showed blunted anterior (ACC), mid (MCC) and posterior cingulate cortex (PCC), voxel-wise connectivity responses to stress compared to neutral cues and blunted PCC response to alcohol compared to neutral cues. Using Cox proportional hazard regression, weaker connectivity in ACC and MCC during neutral exposure was associated with longer time to relapse (better recovery outcome). Similarly, greater connectivity in PCC during alcohol-cue compared to stress cue was associated with longer time to relapse. In Study 2, a sub-group of 30 AUD patients were demographically-matched to 30 healthy control (HC) participants for group comparisons. AUD compared to HC participants showed reduced cingulate connectivity during alcohol and stress cues. Using novel data-driven approaches, the cingulate cortex emerged as a key region in the disruption of functional connectivity during alcohol and stress-cue processing in AUD patients and as a marker of subsequent alcohol relapse.

YNIMG Journal 2017 Journal Article

Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks

• Maolin Qiu
• Dustin Scheinost
• Ramachandran Ramani
• R. Todd Constable

Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing additional evidence that connectivity differences were not fully explained by CBF. In conclusion, these results provide further evidence of alterations in large-scale brain networks are associated with reduced consciousness and suggest that different modalities capture unique aspects of these large scale changes.

YNIMG Journal 2017 Journal Article

Multisite reliability of MR-based functional connectivity

• Stephanie Noble
• Dustin Scheinost
• Emily S. Finn
• Xilin Shen
• Xenophon Papademetris
• Sarah C. McEwen
• Carrie E. Bearden
• Jean Addington

Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i. e. , subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0. 07–0. 17), but increased with increasing scan duration (ICC=0. 21–0. 36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.

YNICL Journal 2016 Journal Article

Language at rest: A longitudinal study of intrinsic functional connectivity in preterm children

• Megan A. Rowlands
• Dustin Scheinost
• Cheryl Lacadie
• Betty Vohr
• Fangyong Li
• Karen C. Schneider
• R. Todd Constable
• Laura R. Ment

BACKGROUND: Preterm (PT) children show early cognitive and language deficits and display altered cortical connectivity for language compared to term (T) children. Developmentally, functional connectivity networks become more segregated and integrated, through the weakening of short-range and strengthening of long-range connections. METHODS: Longitudinal intrinsic connectivity distribution (ICD) values were assessed in PT (n = 13) compared to T children (n = 12) at ages 8 vs. 16 using a Linear Mixed Effects model. Connectivity values in regions generated by the group × age interaction analysis were then correlated to scores on full IQ (FSIQ), verbal IQ (VIQ), verbal comprehension IQ (VCIQ), performance IQ (PIQ), Peabody picture vocabulary test-revised (PPVT-R), and Rapid Naming Composite (RDRL_Cmp). RESULTS: Nine regions were generated by the group × age interaction analysis. PT connectivity significantly increased over time in all but two regions, and they ultimately displayed greater relative connectivity at age 16 than Ts in all areas except the left occipito-temporal cortex (OTC). PTs underwent significant connectivity reductions in the left OTC, which corresponded with worse performance on FSIQ, VIQ, and PIQ. These findings differed from Ts, who did not undergo any significant changes in connectivity over time. CONCLUSIONS: These findings suggest that the developmental alterations in connectivity in PT children at adolescence are both pervasive and widespread. The persistent and worsening cognitive and language deficits noted in the PT subjects may be attributed to the loss of connections in the left OTC.

YNICL Journal 2016 Journal Article

Prenatal stress alters amygdala functional connectivity in preterm neonates

• Dustin Scheinost
• Soo Hyun Kwon
• Cheryl Lacadie
• Gordon Sze
• Rajita Sinha
• R. Todd Constable
• Laura R. Ment

Exposure to prenatal and early-life stress results in alterations in neural connectivity and an increased risk for neuropsychiatric disorders. In particular, alterations in amygdala connectivity have emerged as a common effect across several recent studies. However, the impact of prenatal stress exposure on the functional organization of the amygdala has yet to be explored in the prematurely-born, a population at high risk for neuropsychiatric disorders. We test the hypothesis that preterm birth and prenatal exposure to maternal stress alter functional connectivity of the amygdala using two independent cohorts. The first cohort is used to establish the effects of preterm birth and consists of 12 very preterm neonates and 25 term controls, all without prenatal stress exposure. The second is analyzed to establish the effects of prenatal stress exposure and consists of 16 extremely preterm neonates with prenatal stress exposure and 10 extremely preterm neonates with no known prenatal stress exposure. Standard resting-state functional magnetic resonance imaging and seed connectivity methods are used. When compared to term controls, very preterm neonates show significantly reduced connectivity between the amygdala and the thalamus, the hypothalamus, the brainstem, and the insula (p < 0.05). Similarly, when compared to extremely preterm neonates without exposure to prenatal stress, extremely preterm neonates with exposure to prenatal stress show significantly less connectivity between the left amygdala and the thalamus, the hypothalamus, and the peristriate cortex (p < 0.05). Exploratory analysis of the combined cohorts suggests additive effects of prenatal stress on alterations in amygdala connectivity associated with preterm birth. Functional connectivity from the amygdala to other subcortical regions is decreased in preterm neonates compared to term controls. In addition, these data, for the first time, suggest that prenatal stress exposure amplifies these decreases.

YNIMG Journal 2015 Journal Article

Adaptive mechanisms of developing brain: Cerebral lateralization in the prematurely-born

• Soo Hyun Kwon
• Dustin Scheinost
• Cheryl Lacadie
• Gordon Sze
• Karen C. Schneider
• Feng Dai
• R. Todd Constable
• Laura R. Ment

Preterm birth results in alterations in neural connectivity, but the impact of prematurity on the functional organization of the developing brain has yet to be explored. To test the hypothesis that preterm birth alters cortical organization during the late second and third trimesters of gestation, we interrogated cerebral lateralization at rest in 26 very preterm subjects (birth weight 500–1500g) with no evidence of brain injury and 25 healthy term control subjects at term equivalent age. Employing an unbiased voxel-based measure of functional connectivity, these data demonstrated that cerebral lateralization is impaired in the prematurely-born. At term equivalent age, preterm neonates showed significantly less lateralization in regions subserving both receptive and expressive language, left Brodmann (BA) areas insula–BA22–BA21 and L BA45–BA47 (p <0. 05 corrected for multiple comparisons for both). Exploratory region of interest analyses demonstrated significantly less inter-hemispheric connectivity from L BA22 to R BA22 in preterm infants compared to term controls (p <0. 005) and from R BA22 to its homolog (p <0. 005). L BA22, Wernicke's area, was more strongly connected to R BA39, foreshadowing neural networks for language in preterm subjects at school age, adolescence and young adulthood. For these very preterm neonates born at less than 30weeks' PMA, the degree of prematurity had no influence on lateralization in these differential regions.

YNIMG Journal 2015 Journal Article

Predicting moment-to-moment attentional state

• Monica D. Rosenberg
• Emily S. Finn
• R. Todd Constable
• Marvin M. Chun

Although fluctuations in sustained attention are ubiquitous, most psychological experiments treat them as noise, averaging performance over many trials. The current study uses multi-voxel pattern analysis (MVPA) to decode whether, on each trial of a cognitive task, participants are in an optimal or suboptimal attentional state. During fMRI, participants performed n-back tasks, composed of central face images overlaid on distractor scenes, with low, perceptual, and working memory load. Instructions were to respond to novel faces and withhold response to rare repeats. To index attentional state, reaction time variability was calculated at each correct response. Participants' 50% least variable trials were labeled optimal, or “in the zone, ” and their 50% most erratic trials were labeled suboptimal, or “out of the zone. ” Support vector machine classifiers trained on activity in the default mode network (DMN), dorsal attention network (DAN), and task-relevant fusiform face area (FFA) distinguished in-the-zone and out-of-the-zone trials in all tasks. Consistent with evidence that distractors are processed when central task load is low, parahippocampal place area (PPA) classifiers were only successful in the low load task. Classification in anatomical regions across the brain revealed widespread coding of attentional state. In contrast to these robust pattern analyses, univariate signal in DMN, DAN, FFA, and PPA did not distinguish states, suggesting a nuanced relationship to sustained attention. In sum, MVPA can be used to decode trial-by-trial attentional state throughout much of cortex, helping to characterize how attention network fluctuations correlate with performance variability.

YNIMG Journal 2015 Journal Article

The (in)stability of functional brain network measures across thresholds

• Kathleen A. Garrison
• Dustin Scheinost
• Emily S. Finn
• Xilin Shen
• R. Todd Constable

The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

YNIMG Journal 2014 Journal Article

The impact of image smoothness on intrinsic functional connectivity and head motion confounds

• Dustin Scheinost
• Xenophon Papademetris
• R. Todd Constable

We present a novel method for controlling the effects of group differences in motion on functional connectivity studies. Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool that allows for the assessment of whole-brain functional organization across a wide range of clinical populations. However, as highlighted by recent studies, many measures commonly used in rs-fMRI are highly correlated with subject head movement. A source of this problem is that motion itself, and motion correction algorithms, lead to spatial smoothing, which is then variable across the brain and across subjects or groups dependent upon the amount of motion present during scanning. Studies aimed at elucidating differences between populations that have different head-motion characteristics (e. g. , patients often move more in the scanner than healthy control subjects) are significantly confounded by these effects. In this work, we propose a solution to this problem, uniform smoothing, which ensures that all subject images in a study have equal effective spatial resolution. We establish that differences in the intrinsic smoothness of images across a group can confound connectivity results and link these differences in smoothness to motion. We demonstrate that eliminating these smoothness differences via our uniform smoothing solution is successful in reducing confounds related to the differences in head motion between subjects.

YNIMG Journal 2013 Journal Article

A left cerebellar pathway mediates language in prematurely-born young adults

• R. Todd Constable
• Betty R. Vohr
• Dustin Scheinost
• Jennifer R. Benjamin
• Robert K. Fulbright
• Cheryl Lacadie
• Karen C. Schneider
• Karol H. Katz

Preterm (PT) subjects are at risk for developmental delay, and task-based studies suggest that developmental disorders may be due to alterations in neural connectivity. Since emerging data imply the importance of right cerebellar function for language acquisition in typical development, we hypothesized that PT subjects would have alternate areas of cerebellar connectivity, and that these areas would be responsible for differences in cognitive outcomes between PT subjects and term controls at age 20years. Nineteen PT and 19 term control young adults were prospectively studied using resting-state functional MRI (fMRI) to create voxel-based contrast maps reflecting the functional connectivity of each tissue element in the grey matter through analysis of the intrinsic connectivity contrast degree (ICC-d). Left cerebellar ICC-d differences between subjects identified a region of interest that was used for subsequent seed-based connectivity analyses. Subjects underwent standardized language testing, and correlations with cognitive outcomes were assessed. There were no differences in gender, hand preference, maternal education, age at study, or Peabody Picture Vocabulary Test (PPVT) scores. Functional connectivity (FcMRI) demonstrated increased tissue connectivity in the biventer, simple and quadrangular lobules of the L cerebellum (p<0. 05) in PTs compared to term controls; seed-based analyses from these regions demonstrated alterations in connectivity from L cerebellum to both R and L inferior frontal gyri (IFG) in PTs compared to term controls. For PTs but not term controls, there were significant positive correlations between these connections and PPVT scores (R IFG: r=0. 555, p=0. 01; L IFG: r=0. 454, p=0. 05), as well as Verbal Comprehension Index (VCI) scores (R IFG: r=0. 472, p=0. 04). These data suggest the presence of a left cerebellar language circuit in PT subjects at young adulthood. These findings may represent either a delay in maturation or the engagement of alternative neural pathways for language in the developing PT brain.

YNIMG Journal 2013 Journal Article

Real-time fMRI links subjective experience with brain activity during focused attention

• Kathleen A. Garrison
• Dustin Scheinost
• Patrick D. Worhunsky
• Hani M. Elwafi
• Thomas A. Thornhill
• Evan Thompson
• Clifford Saron
• Gaëlle Desbordes

Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant's experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditators and non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i. e. , focused attention versus mind-wandering). These findings support the feasibility of using rt-fMRI to link objective measures of brain activity with reports of ongoing subjective experience in cognitive neuroscience research, and demonstrate the generalization of expertise in introspective awareness to novel contexts.

YNIMG Journal 2012 Journal Article

Variants in the DYX2 locus are associated with altered brain activation in reading-related brain regions in subjects with reading disability

• Natalie Cope
• John D. Eicher
• Haiying Meng
• Christopher J. Gibson
• Karl Hager
• Cheryl Lacadie
• Robert K. Fulbright
• R. Todd Constable

Reading disability (RD) is a complex genetic disorder with unknown etiology. Genes on chromosome 6p22, including DCDC2, KIAA0319, and TTRAP, have been identified as RD associated genes. Imaging studies have shown both functional and structural differences between brains of individuals with and without RD. There are limited association studies performed between RD genes, specifically genes on 6p22, and regional brain activation during reading tasks. Using fourteen variants in DCDC2, KIAA0319, and TTRAP and exhaustive reading measures, we first tested for association with reading performance in 82 parent-offspring families (326 individuals). Next, we determined the association of these variants with activation of sixteen brain regions of interest during four functional magnetic resonance imaging-reading tasks. We nominally replicated associations between reading performance and variants of DCDC2 and KIAA0319. Furthermore, we observed a number of associations with brain activation patterns during imaging-reading tasks with all three genes. The strongest association occurred between activation of the left anterior inferior parietal lobe and complex tandem repeat BV677278 in DCDC2 (uncorrected p=0. 00003, q=0. 0442). Our results show that activation patterns across regions of interest in the brain are influenced by variants in the DYX2 locus. The combination of genetic and functional imaging data show a link between genes and brain functioning during reading tasks in subjects with RD. This study highlights the many advantages of imaging data as an endophenotype for discerning genetic risk factors for RD and other communication disorders and underscores the importance of integrating neurocognitive, imaging, and genetic data in future investigations.

YNIMG Journal 2011 Journal Article

A whole-brain voxel based measure of intrinsic connectivity contrast reveals local changes in tissue connectivity with anesthetic without a priori assumptions on thresholds or regions of interest

• Roberto Martuzzi
• Ramachandran Ramani
• Maolin Qiu
• Xilin Shen
• Xenophon Papademetris
• R. Todd Constable

The analysis of spontaneous fluctuations of functional magnetic resonance imaging (fMRI) signals has recently gained attention as a powerful tool for investigating brain circuits in a non-invasive manner. Correlation-based connectivity analysis investigates the correlations of spontaneous fluctuations of the fMRI signal either between a single seed region of interest (ROI) and the rest of the brain or between multiple ROIs. To do this, a priori knowledge is required for defining the ROI(s) and without such knowledge functional connectivity fMRI cannot be used as an exploratory tool for investigating the functional organization of the brain and its modulation under different conditions. In this work we examine two indices that provide voxel based maps reflecting the intrinsic connectivity contrast (ICC) of individual tissue elements without the need for defining ROIs and hence require no a priori information or assumptions. These voxel based ICC measures can also be used to delineate regions of interest for further functional or network analyses. The indices were applied to the study of sevoflurane anesthesia-induced alterations in intrinsic connectivity. In concordance with previous studies, the results show that sevoflurane affects different brain circuits in a heterogeneous manner. In addition ICC analyses revealed changes in regions not previously identified using conventional ROI connectivity analyses, probably because of an inappropriate choice of the ROI in the earlier studies. This work highlights the importance of such voxel based connectivity methodology.

YNIMG Journal 2011 Journal Article

Impaired attention and network connectivity in childhood absence epilepsy

• Brendan D. Killory
• Xiaoxiao Bai
• Michiro Negishi
• Clemente Vega
• Marisa N. Spann
• Matthew Vestal
• Jennifer Guo
• Rachel Berman

Patients with childhood absence epilepsy (CAE) often demonstrate impaired interictal attention, even with control of their seizures. No previous study has investigated the brain networks involved in this impairment. We used the continuous performance task (CPT) of attentional vigilance and the repetitive tapping task (RTT), a control motor task, to examine interictal attention in 26 children with CAE and 22 matched healthy controls. Each subject underwent simultaneous 3T functional magnetic resonance imaging–electroencephalography (fMRI–EEG) and CPT/RTT testing. Areas of activation on fMRI during the CPT task were correlated with behavioral performance and used as seed regions for resting functional connectivity analysis. All behavioral measures reflecting inattention were significantly higher in patients. Correlation analysis revealed that impairment on all measures of inattention on the CPT task was associated with decreased medial frontal cortex (MFC) activation during CPT. In addition, analysis of resting functional connectivity revealed an overall decrease within an ‘attention network’ in patients relative to controls. Patients demonstrated significantly impaired connectivity between the right anterior insula/frontal operculum (In/FO) and MFC relative to controls. Our results suggest that there is impaired function in an attention network comprising anterior In/FO and MFC in patients with CAE. These findings provide an anatomical and functional basis for impaired interictal attention in CAE, which may allow the development of improved treatments targeted at these networks.

YNIMG Journal 2011 Journal Article

Preterm birth results in alterations in neural connectivity at age 16 years

• Katherine M. Mullen
• Betty R. Vohr
• Karol H. Katz
• Karen C. Schneider
• Cheryl Lacadie
• Michelle Hampson
• Robert W. Makuch
• Allan L. Reiss

Very low birth weight preterm (PT) children are at high risk for brain injury. Employing diffusion tensor imaging (DTI), we tested the hypothesis that PT adolescents would demonstrate microstructural white matter disorganization relative to term controls at 16years of age. Forty-four PT subjects (600–1250g birth weight) without neonatal brain injury and 41 term controls were evaluated at age 16years with DTI, the Wechsler Intelligence Scale for Children—III (WISC), the Peabody Picture Vocabulary Test—Revised (PPVT), and the Comprehensive Test of Phonological Processing (CTOPP). PT subjects scored lower than term subjects on WISC full scale (p =0. 003), verbal (p =0. 043), and performance IQ tests (p =0. 001), as well as CTOPP phonological awareness (p =0. 004), but scored comparably to term subjects on PPVT and CTOPP Rapid Naming tests. PT subjects had lower fractional anisotropy (FA) values in multiple regions including bilateral uncinate fasciculi (left: p =0. 01; right: p =0. 004), bilateral external capsules (left: p <0. 001; right: p <0. 001), the splenium of the corpus callosum (p =0. 008), and white matter serving the inferior frontal gyrus bilaterally (left: p <0. 001; right: p =0. 011). FA values in both the left and right uncinate fasciculi correlated with PPVT scores (a semantic language task) in the PT subjects (left: r =0. 314, p =0. 038; right: r =0. 336, p =0. 026). FA values in the left and right arcuate fasciculi correlated with CTOPP Rapid Naming scores (a phonologic task) in the PT subjects (left: r =0. 424, p =0. 004; right: r =0. 301, p =0. 047). These data support for the first time that dual pathways underlying language function are present in PT adolescents. The striking bilateral dorsal correlations for the PT group suggest that prematurely born subjects rely more heavily on the right hemisphere than typically developing adults for performance of phonological language tasks. These findings may represent either a delay in maturation or the engagement of alternative neural pathways for language in the developing PT brain.

YNIMG Journal 2010 Journal Article

Functional connectivity and alterations in baseline brain state in humans

• Roberto Martuzzi
• Ramachandran Ramani
• Maolin Qiu
• Nallakkandi Rajeevan
• R. Todd Constable

This work examines the influence of changes in baseline activity on the intrinsic functional connectivity fMRI (fc-fMRI) in humans. Baseline brain activity was altered by inducing anesthesia (sevoflurane end-tidal concentration 1%) in human volunteers and fc-fMRI maps between the pre-anesthetized and anesthetized conditions were compared across different brain networks. We particularly focused on low-level sensory areas (primary somatosensory, visual, and auditory cortices), the thalamus, and pain (insula), memory (hippocampus) circuits, and the default mode network (DMN), the latter three to examine higher-order brain regions. The results indicate that, while fc-fMRI patterns did not significantly differ (p <0. 005; 20-voxel cluster threshold) in sensory cortex and in the DMN between the pre- and anesthetized conditions, fc-fMRI in high-order cognitive regions (i. e. memory and pain circuits) was significantly altered by anesthesia. These findings provide further evidence that fc-fMRI reflects intrinsic brain properties, while also demonstrating that 0. 5 MAC sevoflurane anesthesia preferentially modulates higher-order connections.

YNIMG Journal 2010 Journal Article

Functional connectivity to a right hemisphere language center in prematurely born adolescents

• Eliza H. Myers
• Michelle Hampson
• Betty Vohr
• Cheryl Lacadie
• Stephen J. Frost
• Kenneth R. Pugh
• Karol H. Katz
• Karen C. Schneider

Prematurely born children are at increased risk for language deficits at school age and beyond, but the neurobiological basis of these findings remains poorly understood. Thirty-one PT adolescents (600–1250g birth weight) and 36 T controls were evaluated using an fMRI passive language task and neurodevelopmental assessments including: the Wechsler Intelligence Scale for Children-III (WISC-III), the Peabody Picture Vocabulary Test-Revised (PPVT-R), the Comprehensive Test of Phonological Processing (CTOPP) and the Test of Word Reading Efficiency (TOWRE) at 16years of age. Neural activity was assessed for language processing and the data were evaluated for connectivity and correlations to cognitive outcomes. PT subjects scored significantly lower on all components of the WISC-III (p <0. 05) compared to term subjects, but there was no significant difference in PPVT-R scores between the groups. Functional connectivity (fcMRI) between Wernicke's area (left BA 22) and the right supramarginal gyrus (BA 40) was increased in preterm subjects relative to term controls (p =0. 03), and the strength of this connection was inversely related to performance on both the PPVT-R (R 2 =0. 553, p =0. 002), and the verbal comprehension index (R 2 =0. 439, p =0. 019). Preterm adolescents engage a dorsal right hemisphere region for language at age 16years. Those with the greatest cognitive deficits demonstrate increasing reliance on this alternate pathway.

YNIMG Journal 2009 Journal Article

Alterations in neural connectivity in preterm children at school age

• Yeisid Gozzo
• Betty Vohr
• Cheryl Lacadie
• Michelle Hampson
• Karol H. Katz
• Jill Maller-Kesselman
• Karen C. Schneider
• Bradley S. Peterson

Converging data suggest recovery from injury in the preterm brain. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that cerebral connectivity involving Wernicke's area and other important cortical language regions would differ between preterm (PT) and term (T) control school age children during performance of an auditory language task. Fifty-four PT children (600–1250 g birth weight) and 24 T controls were evaluated using an fMRI passive language task and neurodevelopmental assessments including: the Wechsler Intelligence Scale for Children — III (WISC-III), the Peabody Individual Achievement Test — Revised (PIAT-R) and the Peabody Picture Vocabulary Test — Revised (PPVT-R) at 8 years of age. Neural activity was assessed for language processing and the data were evaluated for connectivity and correlations to cognitive outcomes. We found that PT subjects scored significantly lower on all components of the WISC-III (p <0. 009), the PIAT-R Reading Comprehension test (p =0. 013), and the PPVT-R (p =0. 001) compared to term subjects. Connectivity analyses revealed significantly stronger neural circuits in PT children between Wernicke's area and the right inferior frontal gyrus (R IFG, Broca's area homologue) and both the left and the right supramarginal gyri (SMG) components of the inferior parietal lobules (p ≤0. 02 for all). We conclude that PT subjects employ neural systems for auditory language function at school age differently than T controls; these alterations may represent a delay in maturation of neural networks or the engagement of alternate circuits for language processing.

YNIMG Journal 2009 Journal Article

Similar and dissociable mechanisms for attention to internal versus external information

• Jennifer K. Roth
• Marcia K. Johnson
• Carol L. Raye
• R. Todd Constable

We compared two attentional executive processes: updating, which involved attending to a perceptually present stimulus, and refreshing, which involved attending to a mentally active representation of a stimulus no longer perceptually present. In separate blocks, participants either replaced a word being held in working memory with a different word (update), or they thought back to a just previously seen word that was no longer perceptually present (refresh). Bilateral areas of frontal cortex, supplementary motor area, and parietal cortex were similarly active for both updating and refreshing, suggesting that a common network of areas is recruited to bring information to the current focus of attention. In a direct comparison of update and refresh, regions more active for update than refresh included regions primarily in right frontal cortex, as well as bilateral posterior visual processing regions. Regions more active for refresh than update included regions primarily in left dorsolateral frontal and left temporal cortex and bilateral inferior frontal cortex. These findings help account for the similarity in areas activated across different cognitive tasks and may help specify the particular executive processes engaged in more complex tasks.

YNIMG Journal 2008 Journal Article

Detecting change in BOLD signal between sessions for atlas-based anatomical ROIs

• Robert Buck
• Harsh Singhal
• Jagriti Arora
• Heidi Schlitt
• R. Todd Constable

An algorithm using pre-defined regions of interest (ROIs) to detect differences between sessions in Blood Oxygen-Level Dependent (BOLD) signal is proposed and results from a reproducibility study are reported here. It is important to know whether tests for change have the desired statistical properties, e. g. , low variability between sessions and unbiased false-positive rates, under null conditions so one is confident in any conclusions based on the metric and test used. This study examined three cognitive tasks: Stroop, response inhibition, Sternberg digits, and a visually-cued finger tapping task, in 20 healthy subjects. Each subject had two fMRI sessions, one week apart. A series of ROI summaries was constructed by choosing different proportions of voxels from the ROI and calculating the mean of t values for the selected voxels. The choice of voxels was based on the magnitude of the t values, selecting the maximum value, then the top 1%, and so on until all voxels in the ROI were included. No ROIs were found to have significant differences between sessions based on paired comparison t tests. Generally, the observed false-positive rates were near the expected rates for all summaries and tasks, although the Sternberg and Response Inhibition tasks did have higher false-positive rates when α ≥ 0. 1 for some ROI summaries. This study indicates results are reproducible and also have the desired statistical properties under null conditions.

YNIMG Journal 2008 Journal Article

More accurate Talairach coordinates for neuroimaging using non-linear registration

• Cheryl M. Lacadie
• Robert K. Fulbright
• Nallakkandi Rajeevan
• R. Todd Constable
• Xenophon Papademetris

While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e. g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.

YNIMG Journal 2008 Journal Article

Stop the sadness: Neuroticism is associated with sustained medial prefrontal cortex response to emotional facial expressions

• Brian W. Haas
• R. Todd Constable
• Turhan Canli

Neuroticism is a personality trait associated with negative mood states, sensitivity to negative information, negative appraisal and vulnerability to psychopathology. Previous studies have associated the sustained processing of negative information (words) with individual differences such as rumination and depression but not with personality. In the current study, we aimed to investigate the relationship between neuroticism and changes in sustained patterns of activity within a brain region implicated in emotional self-evaluation and appraisal, the Medial Prefrontal Cortex (MedPFC), when responding to emotional facial expressions (happy, fearful, and sad). We tested whether higher scores of neuroticism are associated with greater sustained patterns of brain activity in the MedPFC when responding to blocks of negative facial expressions. We found that higher scores of neuroticism were associated with greater sustained MedPFC activity throughout blocks of sad facial expressions, but not fearful or happy facial expressions. Based on the relationship between neuroticism and sensitivity to negative information, the current finding identifies a sustained temporal mechanism to this relationship.

YNIMG Journal 2006 Journal Article

Connectivity–behavior analysis reveals that functional connectivity between left BA39 and Broca's area varies with reading ability

• Michelle Hampson
• Fuyuze Tokoglu
• Zhongdong Sun
• Robin J. Schafer
• Pawel Skudlarski
• John C. Gore
• R. Todd Constable

Correlations between temporal fluctuations in MRI signals may reveal functional connectivity between brain regions within individual subjects. Such correlations would be especially useful indices of functional connectivity if they covary with behavioral performance or other subject variables. This study investigated whether such a relationship could be demonstrated in the context of the reading circuit in the brain. The method proved sufficiently powerful to reveal significant correlations between the reading abilities of subjects and the strength of their functional connection between left Brodmann's area 39 and Broca's area during reading. This suggests that the disconnection of the angular gyrus previously reported for dyslexic readers is part of a larger continuum in which poor (but nonimpaired readers) also show reduced connectivity to the region. In addition, it illustrates the potential power of paradigms that examine correlations between behavior and functional brain connections.

YNIMG Journal 2006 Journal Article

Effects of estrogen variation on neural correlates of emotional response inhibition

• Zenab Amin
• C. Neill Epperson
• R. Todd Constable
• Turhan Canli

Despite behavioral evidence that variation in ovarian hormones is associated with changes in affect, the neural basis of these processes is poorly understood. We combined functional magnetic resonance imaging (fMRI) with quantitative analysis of ovarian hormones in a within-subject design to investigate brain activation patterns during affective response inhibition, comparing activation between the early follicular (low estrogen and progesterone) and mid-luteal (high estrogen and progesterone) phases of the menstrual cycle in healthy women. There was significantly increased activation in the anterior cingulate and dorsolateral prefrontal cortex (DLPFC) while inhibiting response to positive words during the luteal, compared to the follicular phase. Furthermore, luteal phase estradiol level positively correlated with DLPFC activation while inhibiting response to positive words and negatively correlated with activation in several structures while inhibiting response to negative words, supporting estrogen's modulation of affective processing.

YNIMG Journal 2006 Journal Article

Gender differences in the neural correlates of response inhibition during a stop signal task

• Chiang-shan Ray Li
• Cong Huang
• R. Todd Constable
• Rajita Sinha

We used functional magnetic resonance imaging to examine gender differences in the neural correlates of response inhibition during a stop signal task. The task has a frequent “go” signal to set up a pre-potent response tendency and a less frequent “stop” signal for subjects to withhold their response. A contrast in brain activation was made between successful and failed inhibitions for individual subjects. We compared 20 men and 20 women matched in age and years of education and in stop signal performance, with stop success rate, post-error slowing and task-related frustration ratings as covariates. The results showed greater activation in men, compared to women, in a wide array of cortical and subcortical areas, including the globus pallidus and motor thalamus during stop signal inhibition. In contrast, no brain regions demonstrated greater activation in women, even at a lower statistical threshold. Moreover, while men activated the medial superior frontal and anterior cingulate cortices, women activated the caudate tail to mediate response inhibition. These results extended gender differences in regional brain activation to response inhibition during a cognitive motor task. Men activated the motor circuitry while women appeared to involve visual association or habit learning during stop signal performance.

YNIMG Journal 2005 Journal Article

Activation of human hippocampal formation reflects success in both encoding and cued recall of paired associates

• Jed A. Meltzer
• R. Todd Constable

Contemporary theories of hippocampal function suggest that both encoding and retrieval of episodic memories may be accomplished by neural circuitry embedded within the same anatomical structures, but neuroimaging support for this hypothesis has been ambiguous. Recent studies suggest that the best available indicators of hippocampal encoding and retrieval operations are selective activations due to novelty, encoding success, and recall success in a paired associate learning paradigm. In the current study, both encoding and cued recall of paired associate words were conducted during a single session of fMRI scanning. Bilateral activation in the medial temporal lobe was detected for encoding word pairs vs. a fixation baseline and for encoding novel word pairs vs. repeated word pairs. These activations were stronger in subjects who successfully memorized more word pairs. In cued recall, greater responses were seen in higher performing subjects. In lower performing subjects, responses were greater to cue words whose paired associate was correctly recalled than to cue words whose correct associate had been forgotten (or not encoded). The difference between correct and incorrect trials was more pronounced on repeated presentations of the same cue words, but not apparent on their first presentation alone. Overlap of encoding and retrieval effects was maximal in the middle of the longitudinal extent of the right hippocampus, with one additional locus of overlap outside the MTL, in left occipitotemporal cortex. The conjunction of these effects suggests that it is correct to view both encoding and recall of associative memories as functions of an integrated hippocampal system.

YNIMG Journal 2001 Journal Article

Comparison of near infrared optical encephalography and fMRI for assessment of language dominance

• Richard P. Kennan
• R. Todd Constable

YNIMG Journal 2000 Journal Article

Brain/behavior relations in working memory

• W. Einar Mencl
• Kenneth Pugh
• Sally Shaywitz
• Bennett Shaywitz
• Robert Fulbright
• R. Todd Constable
• Pawel Skudlarski
• Leonard Katz

YNIMG Journal 1998 Journal Article

Activation Quantitation and ROI Comparisons in Functional MRI

• R. Todd Constable
• P. Skudlarski
• E. Mencl
• K. Pugh
• R. Fulbright
• S. Shaywitz
• B. Shaywitz

YNIMG Journal 1998 Journal Article

THE FUNCTIONAL NEURAL ARCHITECTURE OF COMPONENTS OF ATTENTION IN LANGUAGE PROCESSING TASKS

• Bennett A. Shaywitz
• Sally E. Shaywitz
• Kenneth R. Pugh
• Robert K. Fulbright
• R. Todd Constable
• W. Einar Mencl
• Pawel Skudlarski
• Jack M. Fletcher

YNIMG Journal 1996 Journal Article

Predicting reading performance from neuroimaging profiles: The cerebral basis of phonological effects in printed word identification

• Kenneth R. Pugh
• Bennett A. Shaywitz
• Sally E. Shaywitz
• Donald P. Shankweiler
• Leonard Katz
• Jack M. Fletcher
• Pawel Skudlarski
• Robert K. Fulbright