Discovering and deciphering relationships across disparate data modalities

Vogelstein, Joshua T.; Bridgeford, Eric; Wang, Qing; Priebe, Carey E.; Maggioni, Mauro; Shen, Cencheng

doi:10.7554/elife.41690

Cited by 24 publications

(30 citation statements)

References 83 publications

(204 reference statements)

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“…(delta, r = 0.052, p = 0.19; theta, r = 0.078, p = 0.58; alpha, r = 0.103, p = 0.45; beta, r = 0.102, p = 0.89, broadband, r = 0.12, p = 0.75). We confirm these findings using three other measures of similarity, image intraclass correlation coefficient (I2C2) ( Shou et al, 2013 ), Multiscale Graph Correlation (MGC) algorithm ( Shen et al, 2018 ; Vogelsteinetal., 2019 ) and geodesic distance ( Venkatesh etal., 2020 ) (see Supplement ). Given these results and the distinct clusters of connectivity shown in Fig.…”

Section: Resultssupporting

confidence: 71%

“…The absolute median difference is driven by absolute differences in strength of connectivity at each link, while MDMR compares the similarity of patterns of connectivity within and between networks. Alternative analysis methods such as Joint and Individual Variation Explained (JIVE) ( Lock et al, 2013 ; Yu et al, 2017 ) or Multiscale Graph Correlation ( Shen et al, 2018 ; Vogelstein et al, 2019 ) give us similar results for MDMR ( Fig. A7 ).…”

Section: Discussionmentioning

confidence: 79%

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Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Nentwich

Madsen

et al. 2020

NeuroImage

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A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain “functional connectivity” – the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

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Section: Resultssupporting

confidence: 71%

Section: Discussionmentioning

confidence: 79%

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Nentwich

Madsen

et al. 2020

NeuroImage

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“…However, the nature of such relationships at a specific resolution with phenotypic characteristics should be predefined as our framework only reveals the presence/absence of linear relationships. A recently introduced approach in (Vogelstein et al, ) not only allows identifying the existence of relationships with different natures, but also provides an adaptive framework to identify the most informative components (subsystems) that contribute to such relationships. However, unlike our framework, it may not be able to disentangle the relationships with phenotypic characteristics when (multiple overlapping) subsystems with complex interplays at different resolutions are included.…”

Section: Discussionmentioning

confidence: 99%

Analysis of brain subnetworks within the context of their whole‐brain networks

Bahrami

Laurienti

Simpson

2019

Human Brain Mapping

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Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole‐brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed‐effects modeling framework that allows analyzing subnetworks within the context of their whole‐brain networks, and show that it can better disentangle global (whole‐brain) and local (subnetwork) differences when compared to standard t‐test analyses. T‐test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system‐level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model—quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks—but for local, subnetwork‐level hypotheses.

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“…First, rather than operating on all pairwise distances, Discr only considers the subset that are between measurements of the same item, all other distances are literally discarded, as they do not provide information about the question of interest here. Second, Discr effectively operates on the ranks of distances, rather than the distances, rendering it robust to monotonic transformations of the data [28]. Figure 1 shows three different simulations illustrating the differences between Discr and other reliability statistics, including the fingerprinting index (Fingerprint) [29], intraclass correlation coefficient (ICC) [30], and Kernel [26] (see Appendix A for details).…”

Section: The Discriminability Statisticmentioning

confidence: 99%

“…Indeed, Figure 5 shows that, for virtually every single dataset including sex and age annotation (22 in total), a pipeline with higher Discr tends to preserve more information about both covariates. The amount of information is quantified by the effect size of the distance correlation DCorr (which is exactly equivalent to Kernel [28,39]), a statistic that quantifies the magnitude of association for both linear and nonlinear dependence structures. In contrast, if one were to use either PICC, Kernel, or I2C2…”

Section: Optimizing Discriminability Improves Downstream Inference Pementioning

confidence: 99%

Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics

Bridgeford

Wang

Yang

et al. 2019

Preprint

Self Cite

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The cost of data collection and processing is becoming prohibitively expensive for many research groups across disciplines, a problem that is exacerbated by the dependence of ever larger sample sizes to obtain reliable inferences for increasingly subtle questions. And yet, as more data is available and open access, more researchers desire to analyze it for different questions, often including previously unforeseen questions. To further increase sample sizes, existing datasets are often amalgamated. These reference datasets-datasets that serve to answer many disparate questions for different individuals-are increasingly common and important. Reference pipelines efficiently and flexibly analyze on all the datasets. How can one optimally design these reference datasets and pipelines to yield derivative data that are simultaneously useful for many different tasks? We propose an approach to experimental design that leverages multiple measurements for each distinct item (for example, an individual).The key insight is that each measurement of the same item should be more similar to other measurements of that item, as compared to measurements of any other item. In other words, we seek to optimally discriminate one item from another. We formalize the notion of discriminability, and introduce both a non-parameteric and parametric statistic to quantify the discriminability of potentially multivariate or non-Euclidean datasets. With this notion, one can make optimal decisions-either with regard to acquisition or analysis of data-by maximizing discriminability. Crucially, this optimization can be performed in the absence of any task-specific (or supervised) information. We show that optimizing decisions with respect to discriminability yields improved performance on subsequent inference tasks. We apply this strategy to a brain imaging dataset built by the "Consortium for Reliability and Reproducability" which consists of 24 disparate magnetic resonance imaging datasets, each with up to hundreds of individuals that were imaged multiple times. We show that by optimizing pipelines with respect to discriminability, we improve performance on multiple subsequent inference tasks, even though discriminability does not consider the tasks whatsoever.

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Discovering and deciphering relationships across disparate data modalities

Cited by 24 publications

References 83 publications

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Analysis of brain subnetworks within the context of their whole‐brain networks

Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics

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