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Matthew T. Sutherland

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10 papers

1 author row

YNICL Journal 2021 Journal Article

Neural response to monetary loss among youth with disruptive behavior disorders and callous-unemotional traits in the ABCD study

Amy L. Byrd
Samuel W. Hawes
Rebecca Waller
Mauricio R. Delgado
Matthew T. Sutherland
Anthony S. Dick
Elisa M. Trucco
Michael C. Riedel

Study (N = 11,874; Mage = 9.51; 48% female). Loss-related fMRI activity during the monetary incentive delay task was examined across 16 empirically-derived a priori brain regions (e.g., striatum, amygdala, insula, anterior cingulate cortex, medial prefrontal cortex) and compared across the following groups: (1) typically developing (n = 693); (2) DBD (n = 995), subdivided into those (3) with CU traits (DBD + CU, n = 198), and (4) without CU traits (DBD-only, n = 276). Latent variable modeling was also employed to examine network-level activity. There were no significant between-group differences in brain activity to loss anticipation or receipt. Null findings were confirmed with and without covariates, using alternative grouping approaches, and in dimensional models. Network-level analyses also demonstrated comparable activity across groups during loss anticipation and receipt. Findings suggest that differences in punishment sensitivity among youth with DBD are unrelated to loss anticipation or receipt. More precise characterizations of other aspects punishment sensitivity are needed to understand risk for DBD and CU traits.

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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.

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YNIMG Journal 2017 Journal Article

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Angela R. Laird
Michael C. Riedel
Mershack Okoe
Radu Jianu
Kimberly L. Ray
Simon B. Eickhoff
Stephen M. Smith
Peter T. Fox

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10, 000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i. e. , d=20–300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

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YNIMG Journal 2015 Journal Article

Meta-analytic connectivity and behavioral parcellation of the human cerebellum

Michael C. Riedel
Kimberly L. Ray
Anthony S. Dick
Matthew T. Sutherland
Zachary Hernandez
P. Mickle Fox
Simon B. Eickhoff
Peter T. Fox

The cerebellum historically has been thought to mediate motor and sensory signals between the body and cerebral cortex, yet cerebellar lesions are also associated with altered cognitive behavioral performance. Neuroimaging evidence indicates that the cerebellum contributes to a wide range of cognitive, perceptual, and motor functions. Here, we used the BrainMap database to investigate whole-brainco-activation patterns between cerebellar structures and regions of the cerebral cortex, as well as associations with behavioral tasks. Hierarchical clustering was performed to meta-analytically identify cerebellar structures with similar cortical co-activation, and independently, with similar correlations to specific behavioral tasks. Strong correspondences were observed in these separate but parallel analyses of meta-analytic connectivity and behavioral metadata. We recovered differential zones of cerebellar co-activation that are reflected across the literature. Furthermore, the behaviors and tasks associated with the different cerebellar zones provide insight into the specialized function of the cerebellum, relating to high-order cognition, emotion, perception, interoception, and action. Taken together, these task-basedmeta-analytic results implicate distinct zones of the cerebellum as critically involved in the monitoring and mediation of psychological responses to internal and external stimuli.

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YNIMG Journal 2015 Journal Article

Neural architecture underlying classification of face perception paradigms

Angela R. Laird
Michael C. Riedel
Matthew T. Sutherland
Simon B. Eickhoff
Kimberly L. Ray
Angela M. Uecker
P. Mickle Fox
Jessica A. Turner

We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks.

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YNIMG Journal 2013 Journal Article

Individual differences in amygdala reactivity following nicotinic receptor stimulation in abstinent smokers

Matthew T. Sutherland
Allison J. Carroll
Betty Jo Salmeron
Thomas J. Ross
L. Elliot Hong
Elliot A. Stein

Hyperactive amygdala functioning may underlie emotional dysregulation during smoking abstinence and represents one neurobiological target for pharmacological cessation aids. Available pharmacotherapies (e. g. , nicotine replacement and varenicline) aid only a subset of individuals with smoking cessation and therefore elucidating the neurobiological impact of these medications is critical to expedite improved interventions. In a fMRI study employing a within-subject, double-blind, placebo-controlled design, we assessed task performance and amygdala functioning during an emotional face matching paradigm following administration of nicotine and varenicline to 24 abstinent smokers and 20 nonsmokers. All participants underwent ~17days of varenicline and placebo pill administration and were scanned, on different days under each condition, wearing a transdermal nicotine or placebo patch. During the amygdala reactivity paradigm, nicotinic acetylcholine receptor (nAChR) stimulation by nicotine and varenicline decreased reaction time (RT) in abstinent smokers but not in nonsmokers. When considering all smokers as a single homogenous group, no drug-induced effects on amygdala reactivity were detected. However, in an exploratory analysis we parsed participants into subgroups according to individual differences in the propensity to demonstrate stable performance augmentation following nAChR stimulation (stable RT-improvers [SI] vs. variable RT-improvers [VI]). Using this exploratory approach, drugs appeared to modulate amygdala reactivity in only one smoker subgroup but not in either nonsmoker subgroup. Specifically, in the SI-smoker cohort abstinence-induced elevated amygdala reactivity was down-regulated by nAChR stimulation. In contrast, varenicline and nicotine did not modulate amygdala functioning in the VI-smoker cohort who displayed moderate levels of amygdala reactivity in the absence of drug administration. These results suggest that pharmacotherapies most robustly dampened amygdala functioning in smokers appearing susceptible to abstinence-induced effects. Such findings provide a step towards fractionating the smoker phenotype by discrete neurobiological characteristics.

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YNIMG Journal 2012 Journal Article

Resting state functional connectivity in addiction: Lessons learned and a road ahead

Matthew T. Sutherland
Meredith J. McHugh
Vani Pariyadath
Elliot A. Stein

Despite intensive scientific investigation and public health imperatives, drug addiction treatment outcomes have not significantly improved in more than 50years. Non-invasive brain imaging has, over the past several decades, contributed important new insights into the neuroplastic adaptations that result from chronic drug intake, but additional experimental approaches and neurobiological hypotheses are needed to better capture the totality of the motivational, affective, cognitive, genetic and pharmacological complexities of the disease. Recent advances in assessing network dynamics through resting-state functional connectivity (rsFC) may allow for such systems-level assessments. In this review, we first summarize the nascent addiction-related rsFC literature and suggest that in using this tool, circuit connectivity may inform specific neurobiological substrates underlying psychological dysfunctions associated with reward, affective and cognitive processing often observed in drug addicts. Using nicotine addiction as an exemplar, we subsequently provide a heuristic framework to guide future research by linking recent findings from intrinsic network connectivity studies with those interrogating nicotine's neuropharmacological actions. Emerging evidence supports a critical role for the insula in nicotine addiction. Likewise, the anterior insula, potentially together with the anterior cingulate cortex, appears to pivotally influence the dynamics between large-scale brain networks subserving internal (default-mode network) and external (executive control network) information processing. We suggest that a better understanding of how the insula modulates the interaction between these networks is critical for elucidating both the cognitive impairments often associated with withdrawal and the performance-enhancing effects of nicotine administration. Such an understanding may be usefully applied in the design and development of novel smoking cessation treatments.

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YNIMG Journal 2006 Journal Article

Reliable detection of bilateral activation in human primary somatosensory cortex by unilateral median nerve stimulation

Matthew T. Sutherland
Akaysha C. Tang

In non-human primates, a bilateral representation of unilaterally presented somatosensory information can be found at the lowest level of cortical processing as indicated by the presence of neurons with bilateral receptive fields in the hand region of primary somatosensory (SI) cortex. In humans, such bilateral activation of SI is considered controversial due to highly variable detection rates for the much weaker ipsilateral response across different studies (ranging from 3% to 100%). Second-order blind identification (SOBI) is a blind source separation algorithm that has been successfully used to isolate neuronal signals from functionally distinct brain regions, including the left- and right-SI. SOBI-aided extraction of left- and right-SI responses to median nerve stimulation from high-density EEG has been previously validated against the fMRI and MEG literature. Here, we applied SOBI to EEG data and examined whether relatively weaker ipsilateral activations could be reliably detected across subjects. In single subject analysis, statistically significant somatosensory evoked potentials (SEPs) in response to unilateral stimulation were detected from both SI contralateral to and SI ipsilateral to the side of stimulation. Furthermore, these ipsilateral responses were observed in both the left and right hemispheres of all 10 subjects studied. Together these results demonstrate that unilateral stimulation of the median nerve, whether applied to the left or right wrist, can activate both the left- and right-SI, raising the possibility that in humans, unilateral sensory input may be bilaterally represented at the lowest level of cortical processing.

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YNIMG Journal 2005 Journal Article

Recovery of correlated neuronal sources from EEG: The good and bad ways of using SOBI

Akaysha C. Tang
Jing-Yu Liu
Matthew T. Sutherland

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that has been applied to MEG and EEG data collected during a range of sensory, motor, and cognitive tasks. SOBI can decompose mixtures of electric or magnetic signals by utilizing detailed temporal structures present in the continuously recorded signals. Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices. Here, we present empirical findings from high-density EEG data (128 channels) to show that SOBI's ability to recover correlated neuronal sources critically depends on the appropriate use of these temporal delay parameters. Specifically, we applied SOBI to EEG data collected during correlated activation of the left and right primary somatosensory cortices (SI). We show that separation of signals originating from the left and right SI is better achieved by using a large number and a wide range of temporal delays between a few and several hundred milliseconds when compared to results using various subsets of these delays. The paper also offers non-mathematician/engineer users a gentle introduction to the inner workings of SOBI.

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YNIMG Journal 2005 Journal Article

Validation of SOBI components from high-density EEG

Akaysha C. Tang
Matthew T. Sutherland
Christopher J. McKinney

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.

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