Biology subjects

Calhoun, V. D.

Publications and source records attributed to Calhoun, V. D..

96 records · Page 6Linked to original sources

bioRxiv2018

Transdiagnostic multimodal neuroimaging in psychosis: structural, resting-state, and task MRI correlates of cognitive control

BackgroundPsychotic disorders, including schizophrenia and bipolar disorder, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control related neural alterations have been studied in psychosis. However, studies are typically unimodal and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control related neural variation in psychosis.\n\nMethodsStructural, resting, and working memory task imaging and behavioral data for 31 controls, 27 bipolar, and 23 schizophrenia patients were collected and processed identically to the Human Connectome Project (HCP), enabling identification of relationships with prior multimodal work. Two cognitive control related independent components (ICs) derived from the HCP using multiset canonical correlation analysis + joint independent component analysis (mCCA+jICA) were used to predict performance in psychosis. de novo mCCA+jICA was performed, and resultant IC weights were correlated with cognitive control.\n\nResultsA priori ICs significantly predicted cognitive control in psychosis (3/5 modalities significant). De novo mCCA+jICA identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular, somatomotor, cingulate, and visual regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested de novo correlations with cognitive control were primarily influenced by schizophrenia patients.\n\nConclusionsA priori components partially predicted performance in transdiagnostic psychosis and de novo analyses identified novel contributions in somatomotor and visual regions in schizophrenia. Together, results suggest joint contributions across modalities related to cognitive control across the healthy-to-psychosis spectrum.

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bioRxiv2017

Replicability of time-varying connectivity patterns in large resting state fMRI samples

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured surrogate datasets establishes that the correlation structures in the data do not arise by chance. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance.

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bioRxiv2017

Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations

Studies of resting state functional MRI (rs-fRMI) are increasingly focused on \"dynamics\", or on those properties of brain activation that manifest and vary on timescales shorter than the scans full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this short paper. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e. their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e. timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the epochal temporal variation that contributes heavily to dynamic functional connectivity estimates and is and often taken as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations versus across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence or functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.

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bioRxiv2017

Salience network dynamics underlying successful resistance of temptation

Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate towards configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics, and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control.

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bioRxiv2017

Multimodal Neural Correlates Of Cognitive Control In The Human Connectome Project

Cognitive control is a construct that refers to the set of functions that enable decisionmaking 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.

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bioRxiv2017

Mindfulness and dynamic functional neural connectivity in children and adolescents

BackgroundMindfulness is a non-judgmental, present-centered awareness and acceptance of ones thoughts, feelings, and bodily sensations. Interventions that promote mindfulness consistently show salutatory effects on cognition and psychological wellbeing in adults, and more recently, in children. Understanding the neurobiological mechanisms underlying mindfulness in children may allow for more judicious application of these techniques in clinical and educational settings.\n\nMethodsUsing multi-echo/multi-band fMRI, we measured resting-state connectivity and tested the hypothesis that the association between mindfulness and anxiety in children (N=42) will relate to static and dynamic interactions between large-scale neural networks considered central to neurocognitive functioning and implicated in mindfulness in adults (default mode [DMN], salience and emotion [SEN], and central executive networks [CEN]).\n\nResultsMindfulness was related to dynamic but not static connectivity in children. Specifically, more mindful children transitioned more between brain states over the course of the scan, spent overall less time in a certain connectivity state (state 2), and showed a state-specific reduction in SEN-right CEN connectivity (state 4). Results of a separate measure of present-focused thought during the resting-state were consistent with these results, suggesting state-trait convergence. Finally, the number of state transitions mediated the link between higher mindfulness and lower anxiety, suggesting that flexibility in transitioning between neural states may bridge the well-established link between mindfulness and anxiety in children.\n\nConclusionsResults provide new insights into neural mechanisms underlying benefits of mindfulness on psychological health in children, and suggest that mindfulness relates to functional neural dynamics and interactions between neurocognitive networks, over time.

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