Author name cluster

Bryon A. Mueller

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15 papers

1 author row

YNIMG Journal 2025 Journal Article

Characterizing the effects of age, puberty, and sex on variability in resting-state functional connectivity in late childhood and early adolescence

Kelly A. Duffy
Andrea Wiglesworth
Donovan J. Roediger
Ellery Island
Bryon A. Mueller
Monica Luciana
Bonnie Klimes-Dougan
Kathryn R. Cullen

Understanding the relative influences of age, pubertal development, and sex assigned at birth on brain development is a key priority of developmental neuroscience given the complex interplay of these factors in the onset of psychopathology. Previous research has investigated how these factors relate to static (time-averaged) functional connectivity (FC), but little is known about their relationship with dynamic (time-varying) FC. The present study aimed to investigate the unique and overlapping roles of these factors on dynamic FC in children aged approximately 9 to 14 in the ABCD Study using a sample of 5122 low-motion resting-state scans (from 4136 unique participants). Time-varying correlations in the frontolimbic, default mode, and dorsal and ventral corticostriatal networks, estimated using the Dynamic Conditional Correlations (DCC) method, were used to calculate variability of within- and between-network connectivity and of graph theoretical measures of segregation and integration. We found decreased variability in global efficiency across the age range, and increased variability within the frontolimbic network driven primarily by those assigned female at birth (AFAB). AFAB youth specifically also showed increased variability in several other networks. Controlling for age, both advanced pubertal development and being AFAB were associated with decreased variability in all within- and between-network correlations and increased variability in measures of network segregation. These results potentially suggest advanced brain maturation in AFAB youth, particularly in key networks related to psychopathology, and lay the foundation for future investigations of dynamic FC.

YNICL Journal 2025 Journal Article

Normative modeling of brain MRI data identifies small subcortical volumes and associations with cognitive function in youth with fetal alcohol spectrum disorder (FASD)

Blake A. Gimbel
Donovan J. Roediger
Mary E. Anthony
Abigail M. Ernst
Kent A. Tuominen
Bryon A. Mueller
Erik de Water
Madeline N. Rockhold

AIM: To quantify regional subcortical brain volume anomalies in youth with fetal alcohol spectrum disorder (FASD), assess the relative sensitivity and specificity of abnormal volumes in FASD vs. a comparison group, and examine associations with cognitive function. METHOD: Participants: 47 children with FASD and 39 typically-developing comparison participants, ages 8-17 years, who completed physical evaluations, cognitive and behavioral testing, and an MRI brain scan. A large normative MRI dataset that controlled for sex, age, and intracranial volume was used to quantify the developmental status of 7 bilateral subcortical regional volumes. Z-scores were calculated based on volumetric differences from the normative sample. T-tests compared subcortical volumes across groups. Percentages of atypical volumes are reported as are sensitivity and specificity in discriminating groups. Lastly, Pearson correlations examined the relationships between subcortical volumes and neurocognitive performance. RESULTS: Participants with FASD demonstrated lower mean volumes across a majority of subcortical regions relative to the comparison group with prominent group differences in the bilateral hippocampi and bilateral caudate. More individuals with FASD (89%) had one or more abnormally small volume compared to 72% of the comparison group. The bilateral hippocampi, bilateral putamen, and right pallidum were most sensitive in discriminating those with FASD from the comparison group. Exploratory analyses revealed associations between subcortical volumes and cognitive functioning that differed across groups. CONCLUSION: In this sample, youth with FASD had a greater number of atypically small subcortical volumes than individuals without FASD. Findings suggest MRI may have utility in identifying individuals with structural brain anomalies resulting from PAE.

YNIMG Journal 2024 Journal Article

tDCS-enhanced cognitive training improves attention and alters connectivity in control and somatomotor networks: A triple blind study

Rebecca Kazinka
Donovan Roediger
Lei Xuan
Lingyan Yu
Bryon A. Mueller
Jazmin Camchong
Alexander Opitz
Angus MacDonald

BACKGROUND: Executive dysfunction such as inattention or forgetfulness can lead to disruptions in a person's daily functioning and quality of life. OBJECTIVE/HYPOTHESIS: This triple-blinded randomized clinical trial assessed the efficacy of bifrontal (over the forehead) transcranial direct current stimulation (tDCS) concurrent with cognitive training to improve cognitive performance in a healthy sample. METHODS: Fifty-eight participants were randomly assigned to one of three stimulation conditions (2 mA left anode-right cathode, 2 mA right anode-left cathode, or sham), which was administered with cognitive training tasks 3x/week over 12 weeks with assessments at baseline, midpoint (6 weeks), and post-training (12 weeks). We assessed cognitive performance, functional connectivity, and the influence of individual differences in training advancement. RESULTS: Forty participants completed training. We found that at midpoint and post, all groups improved significantly on overall cognitive performance. The left anode group's attention & vigilance score improved significantly at post, but the other two groups did not. Greater attention training advancement predicted attention improvement by post, most notably in the left anode group. Finally, within-network connectivity decreased in the control network and increased in the somatomotor network across all groups. CONCLUSIONS: These results suggest that, given cognitive training, the left anode montage is more effective at improving attention than the right anode montage and sham. Future research may focus on the application of the left anode montage during cognitive training to assess its effectiveness in improving cognition in neuropsychiatric disorders.

YNICL Journal 2023 Journal Article

Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study

Xing Meng
Armin Iraji
Zening Fu
Peter Kochunov
Aysenil Belger
Judy M. Ford
Sara McEwen
Daniel H. Mathalon

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.

YNIMG Journal 2022 Journal Article

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

Eric Rawls
Erich Kummerfeld
Bryon A. Mueller
Sisi Ma
Anna Zilverstand

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

YNIMG Journal 2021 Journal Article

The psychosis human connectome project: An overview

Caroline Demro
Bryon A. Mueller
Jerillyn S. Kent
Philip C. Burton
Cheryl A. Olman
Michael-Paul Schallmo
Kelvin O. Lim
Scott R. Sponheim

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.

YNICL Journal 2020 Journal Article

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI

Bhaskar Sen
Gail A. Bernstein
Bryon A. Mueller
Kathryn R. Cullen
Keshab K. Parhi

This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.

YNIMG Journal 2019 Journal Article

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Barnaly Rashid
Jiayu Chen
Ishtiaque Rashid
Eswar Damaraju
Jingyu Liu
Robyn Miller
Oktay Agcaoglu
Theo G.M. van Erp

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i. e. , time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

YNICL Journal 2017 Journal Article

Cortical gyrification is abnormal in children with prenatal alcohol exposure

Timothy J. Hendrickson
Bryon A. Mueller
Elizabeth R. Sowell
Sarah N. Mattson
Claire D. Coles
Julie A. Kable
Kenneth L. Jones
Christopher J. Boys

OBJECTIVES: Prenatal alcohol exposure (PAE) adversely affects early brain development. Previous studies have shown a wide range of structural and functional abnormalities in children and adolescents with PAE. The current study adds to the existing literature specifically on cortical development by examining cortical gyrification in a large sample of children with PAE compared to controls. Relationships between cortical development and intellectual functioning are also examined. EXPERIMENTAL DESIGN: Included were 92 children with PAE and 83 controls ages 9-16 from four sites in the Collaborative Initiative on FASD (CIFASD). All PAE participants had documented heavy PAE. All underwent a formal evaluation of physical anomalies and dysmorphic facial features. MRI data were collected using modified matched protocols on three platforms (Siemens, GE, and Philips). Cortical gyrification was examined using a semi-automated procedure. PRINCIPAL OBSERVATIONS: Whole brain group comparisons using Monte Carlo z-simulation for multiple comparisons showed significantly lower cortical gyrification across a large proportion of the cerebral cortex amongst PAE compared to controls. Whole brain comparisons and ROI based analyses showed strong positive correlations between cortical gyrification and IQ (i.e. less developed cortex was associated with lower IQ). CONCLUSIONS: Abnormalities in cortical development were seen across the brain in children with PAE compared to controls. Cortical gyrification and IQ were strongly correlated, suggesting that examining mechanisms by which alcohol disrupts cortical formation may yield clinically relevant insights and potential directions for early intervention.

YNIMG Journal 2016 Journal Article

The Function Biomedical Informatics Research Network Data Repository

David B. Keator
Theo G.M. van Erp
Jessica A. Turner
Gary H. Glover
Bryon A. Mueller
Thomas T. Liu
James T. Voyvodic
Jerod Rasmussen

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1. 5 to 4T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

YNIMG Journal 2014 Journal Article

Comparison of statistical tests for group differences in brain functional networks

Junghi Kim
Jeffrey R. Wozniak
Bryon A. Mueller
Xiaotong Shen
Wei Pan

Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can also be applied to adjust for covariates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

YNIMG Journal 2013 Journal Article

Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia

Jing Sui
Hao He
Godfrey D. Pearlson
Tülay Adali
Kent A. Kiehl
Qingbao Yu
Vince P. Clark
Eduardo Castro

Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called “multimodal CCA+joint ICA”, to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared “mCCA+jICA” with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)–Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed “mCCA+jICA” method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.

YNIMG Journal 2012 Journal Article

Altered resting state complexity in schizophrenia

Danielle S. Bassett
Brent G. Nelson
Bryon A. Mueller
Jazmin Camchong
Kelvin O. Lim

The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

YNIMG Journal 2010 Journal Article

Multi-site characterization of an fMRI working memory paradigm: Reliability of activation indices

Anastasia Yendiki
Douglas N. Greve
Stuart Wallace
Mark Vangel
Jeremy Bockholt
Bryon A. Mueller
Vince Magnotta
Nancy Andreasen

Neuroimaging studies are facilitated significantly when it is possible to recruit subjects and acquire data at multiple sites. However, the use of different scanners and acquisition protocols is a potential source of variability in multi-site data. In this work we present a multi-site study of the reliability of fMRI activation indices, where 10 healthy volunteers were scanned at 4 different sites while performing a working memory paradigm. Our results indicate that, even with different scanner manufacturers and field strengths, activation variability due to site differences is small compared to variability due to subject differences in this cognitive task, provided we choose an appropriate activation measure.

YNIMG Journal 2008 Journal Article

The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents

Ryan L. Muetzel
Paul F. Collins
Bryon A. Mueller
Ann M. Schissel
Kelvin O. Lim
Monica Luciana

Cross-sectional and longitudinal volumetric studies suggest that the corpus callosum (CC) continues to mature structurally from infancy to adulthood. Diffusion tensor imaging (DTI) provides in vivo information about the directional organization of white matter microstructure and shows potential for elucidating even more subtle brain changes during adolescent development. We used DTI to examine CC microstructure in healthy right-handed adolescents (n =92, ages 9–24 years) and correlated the imaging data with motor task performance. The primary DTI variable was fractional anisotropy (FA), which reflects the degree of white matter’s directional organization. Participants completed an alternating finger tapping test to assess interhemispheric transfer and motor speed. Task performance was significantly correlated with age. Analyses of variance indicated that 9- to 11-year-olds generally performed worse than each of the older groups. Males outperformed females. Significant positive correlations between age and FA were observed in the splenium of the CC, which interconnects posterior cortical regions. Analyses of variance indicated that individuals older than 18 years had significantly higher FA than 9- to 11-year-olds. FA levels in the genu and splenium correlated significantly with task performance. Regression analyses indicated that bimanual coordination was significantly predicted by age, gender, and splenium FA. Decreases in alternating finger tapping time and increases in FA likely reflect increased myelination in the CC and more efficient neuronal signal transmission. These findings expand upon existing neuroimaging reports of CC development by showing associations between bimanual coordination and white matter microstructural organization in an adolescent sample.