Vince Calhoun

Vince Calhoun

PhD

About

1,338
Publications
250,415
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66,846
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Publications

Publications (1,338)
Conference Paper
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Model interpretation is an active research area, aiming to unravel the black box of deep learning models. One common approach, saliency, leverages the gradients of the model to produce a per-input map highlighting the features most important for a correct prediction. However, saliency faces challenges in recurrent models due to the "vanishing salie...
Article
Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Prev...
Article
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Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modalit...
Article
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The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source‐based...
Preprint
Our recent findings show that functional organizations evolve spatially over time, highlighting the importance of considering within-subject spatial variations and dynamic functional parcellations in brain functional analyses. Meanwhile, a considerable level of multi-functionality suggests the need for overlapping brain parcellations. In this work,...
Article
Background: Longitudinal neuroimaging studies have demonstrated that adolescence is a crucial developmental period of continued brain growth and change. Motivated by both achievements in graph signal processing and recent evidence that some brain areas act as hubs connecting functionally specialized systems, we propose an approach to detect these...
Article
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Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patte...
Preprint
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The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move towards a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely-used...
Article
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Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning techno...
Preprint
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Epigenetic mechanisms, such as DNA methylation (DNAm), have gained increasing attention in the field of neuroimaging as a potential biomarker of – or mechanism mediating – genetic and environmental influences on the brain. Yet, the extent to which DNAm associates with individual differences in the brain – the most relevant organ for the study of ps...
Preprint
Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA expl...
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Relative to the general population, criminal offenders have a higher risk of suicide. Neurobiological deficits related to suicidal behavior have been identified in the general population, but unexamined in offenders to date. We examined the association between brain morphology and suicidal behavior in adult male criminal offenders. Brain morphology...
Article
Background Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several clustering validity index (CVI) methods to determine the best clustering partition exists, th...
Preprint
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Human brain development is a complex and dynamic process that is affected by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the d...
Article
Background: Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes preven...
Article
There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic...
Preprint
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Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revea...
Article
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From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as "polarization." These are whole-brain states in which the voxelwise-normalized B...
Preprint
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Functional network connectivity (FNC) obtained from resting-state functional magnetic resonance imaging (fMRI) data have been commonly used to study mental disorders in neuroimaging applications. Likewise, generative adversarial networks (GANs) have performed well in multiple classification benchmark tasks. However, the application of GANs to fMRI...
Article
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With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithm...
Preprint
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Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional approaches to this problem either proceed from handcrafted features or require large datasets to combat the m >> n problem. In this paper, we show that the source of the pro...
Article
Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus ch...
Article
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The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of st...
Article
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Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year‐wise estimation of 5,967 subjects from 13...
Preprint
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Visualization of high dimensional large-scale datasets via an embedding into a 2D map is a powerful exploration tool for assessing latent structure in the data and detecting outliers. It plays a vital role in neuroimaging field because sometimes it is the only way to perform quality control of large dataset. There are many methods developed to perf...
Preprint
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise una...
Preprint
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Acute ischemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for loss of specialized neural tissue and function. Static resting-state functional magnetic resonance imaging (fMRI) studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced cha...
Article
Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give ri...
Article
Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with...
Article
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Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using F...
Preprint
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The human brain has the ability of changing its wiring configuration by increasing or decreasing functional connectivity strength between specific areas. Variable but recurring configuration patterns in dynamic functional connectivity have been observed during resting fMRI experiments, patterns which are defined as dynamic brain states. The questio...
Preprint
Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses th...
Preprint
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood. A traditional approach in neuroimaging data analysis is to make simplifica...
Preprint
Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. Standardized approaches for capturing reproducible and comparable biomarkers are greatly needed. Here, we propose a method, NeuroMark , which leverages a priori-driven independent component analysis to effectively extract function...
Article
Negative symptoms are core contributors to vocational and social deficits in schizophrenia (SZ). Available antipsychotic medications typically fail to reduce these symptoms. The neurohormone oxytocin (OT) is a promising treatment for negative symptoms, given its role in complex social behaviors mediated by the amygdala. In sample 1, we used a doubl...
Article
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Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta-...
Article
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series—functional connectivity (FC) methods—are likely a good starting point for estimating brain network interactions. Y...
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The current study set out to investigate the dynamic functional connectome in relation to long‐term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting‐state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 h...
Article
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Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high nois...
Article
Objective: Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor...
Article
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Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duratio...
Article
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Background: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. Methods: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequence...
Article
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Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this st...
Article
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Functional connectivity is one of the most widely used tools for investigating brain changes due to schizophrenia. Previous studies have identified abnormal functional connectivity in schizophrenia patients at the resting state brain network level. This study tests the existence of functional connectivity effects at whole brain and domain levels. D...
Article
Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task si...
Article
COVER ILLUSTRATION Brain networks evolve spatially over time. The spatially fluid chronnectome focuses on the variation in spatial patterns at the voxel level within each network. Here red, blue, and green represent three spatial states of the default mode network. Each spatial state represents a distinct spatial pattern of the default mode network...
Article
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Background: The N400 event-related potential (ERP) is triggered by meaningful stimuli that are incongruous, or unmatched, with their semantic context. Functional magnetic resonance imaging (fMRI) studies have identified brain regions activated by semantic incongruity, but their precise links to the N400 ERP are unclear. In schizophrenia (SZ), N400...
Article
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Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic stat...
Preprint
Objective: Longitudinal neuroimaging studies have demonstrated that adolescence is the crucial developmental epoch of continued brain growth and change. A large number of researchers dedicate to uncovering the mechanisms about brain maturity during adolescence. Motivated by both achievement in graph signal processing and recent evidence that some b...
Article
Full-text available
The kernel canonical correlation analysis based U-statistic (KCCU) is being used to detect nonlinear gene-gene co-associations. Estimating the variance of the KCCU is however computationally intensive. In addition, the kernel canonical correlation analysis (kernel CCA) is not robust to contaminated data. Using a robust kernel mean element and a rob...
Article
Objective: Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize the intrinsic association, and thus can boost the learning performance. Although several multi-task based learning models h...
Article
Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut-microbiome-brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the...
Article
Numerous lines of evidence have shown that cognitive processes engaged during response inhibition tasks are associated with structure and functional integration of regions within fronto-parietal networks. However, while prior studies have started to characterize how intrinsic connectivity during resting state differs between boys and girls, compara...
Article
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There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted...
Article
Objective: To investigate the dynamic functional connectivity of thalamo-cortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity. Methods: We investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy control...
Article
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Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of diff...