Advancing functional connectivity research from association to causation

Reid, Andrew; Headley, Drew B.; Mill, Ravi D.; Sanchez-Romero, Ruben; Uddin, Lucina Q.; Marinazzo, Daniele; Lurie, Daniel J.; Valdés‐Sosa, Pedro A.; Hanson, Stephen José; Biswal, Bharat B.; Calhoun, Vince D.; Poldrack, Russell A.; Cole, Jennifer

doi:10.1038/s41593-019-0510-4

Cited by 245 publications

(273 citation statements)

References 93 publications

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“…Functional connectivity studies using a top down case-control approach (eg. autism versus control) have characterize large-scale brain network changes associated with diseases, but this framework is unable to describe the directionality of this relationship 38…”

Section: Discussionmentioning

confidence: 99%

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Moreau

Urchs

Orban

et al. 2019

Preprint

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16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) networks remains unclear.We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify major dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level, and their effect-sizes were twice as large as those of idiopathic conditions. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD.Individuals with higher similarity to deletion FC-signatures exhibited worse behavioral and cognitive symptoms. These seemingly distinct neuropsychiatric mutations showed similar gene co-expression patterns and converged on FC dimensions, that may represent mechanistic building blocks shared across idiopathic conditions.

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Section: Discussionmentioning

confidence: 99%

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Moreau

Urchs

Orban

et al. 2019

Preprint

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“…Two unconnected nodes will have a negative spurious partial correlation from conditioning on a collider if their connectivity coefficients with the collider have the same sign. In contrast, they will have a positive spurious partial correlation if their associations have opposite signs (Reid et al, 2019;Smith, 2012) . Anatomical studies in non-human primates have established that most long-range cortico-cortical connections are positive (i.e., glutamatergic) (Barbas, 2015) , such that we can reasonably assume that most true connections among brain regions have the same sign (positive).…”

Section: Figures 2e and 3ementioning

confidence: 99%

“…Measures such as mutual information and conditional mutual information, for example, will produce spurious edges in the presence of a confounder or a collider respectively. Here we focus on linear bivariate correlation and partial correlation since they are two of the most-used methods to infer brain connectivity from functional MRI data (Cole, Ito, Bassett, & Schultz, 2016;Marrelec et al, 2006;Reid et al, 2019;Ryali, Chen, Supekar, & Menon, 2012) . Similar considerations apply to all brain measurement methods, such as electroencephalography, magnetoencephalography, or multi-unit recording.…”

Section: Introductionmentioning

confidence: 99%

Combining multiple functional connectivity methods to improve causal inferences

Sanchez-Romero

Cole

2019

Preprint

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Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series-functional connectivity (FC) methods-are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives since they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, though this was not a problem for bivariate correlations. This led us to propose a new combined functional connectivity method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences.

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“…In a SEED experiment, the sixty-two-channel EEG recordings of the participants took place while they watched the Journal of Brain Sciences 2020, 10, 8 3 of 32 fifteen movie clips (four minutes in duration) whose contents elicited three distinct affect: Negative, Neutral, and Positive.Although previous research aimed at identifying the functional interaction among brain regions, it mostly framed such an interactivity in terms of statistical association (e.g., correlation) [67]. However, this approach to the study of brain cortical regional interactivity is problematic since such associations as correlation can arise in a variety of ways that do not entail causal relation (i.e., directional flow of information) [67]. As a result, they do not allow for understanding the mapping between such associations and their underlying neural substrates [67,68].…”

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confidence: 99%

“…However, this approach to the study of brain cortical regional interactivity is problematic since such associations as correlation can arise in a variety of ways that do not entail causal relation (i.e., directional flow of information) [67]. As a result, they do not allow for understanding the mapping between such associations and their underlying neural substrates [67,68]. Addressing these shortcomings can be facilitated by utilization of such approaches as Granger causality that provide means to establish directional relations between the brain regions.…”

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confidence: 99%

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

2019

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Over the past few decades, the quest for discovering the brain substrates of the affect to understand the underlying neural basis of the human's emotions has resulted in substantial and yet contrasting results. Whereas some point at distinct and independent brain systems for the Positive and Negative affects, others propose the presence of flexible brain regions. In this respect, there are two factors that are common among these previous studies. First, they all focused on the change in brain activation, thereby neglecting the findings that indicate that the stimuli with equivalent sensory and behavioral processing demands may not necessarily result in differential brain activation. Second, they did not take into consideration the brain regional interactivity and the findings that identify that the signals from individual cortical neurons are shared across multiple areas and thus concurrently contribute to multiple functional pathways. To address these limitations, we performed Granger causal analysis on the electroencephalography (EEG) recordings of the human subjects who watched movie clips that elicited Negative, Neutral, and Positive affects. This allowed us to look beyond the brain regional activation in isolation to investigate whether the brain regional interactivity can provide further insights for understanding the neural substrates of the affect. Our results indicated that the differential affect states emerged from subtle variation in information flow of the brain cortical regions that were in both hemispheres. They also showed that these regions that were rather common between affect states than distinct to a specific affect were characterized with both shortas well as long-range information flow. This provided evidence for the presence of simultaneous integration and differentiation in the brain functioning that leads to the emergence of different affects. These results are in line with the findings on the presence of intrinsic large-scale interacting brain networks that underlie the production of psychological events. These findings can help advance our understanding of the neural basis of the human's emotions by identifying the signatures of differential affect in subtle variation that occurs in the whole-brain cortical flow of information.the Journal of Brain Sciences 2020, 10, 8 2 of 32 hypothesizes that a single brain region consistently and specifically activates across instances of a single emotion category/state (e.g., Positive state). It also argues that such states are biologically basic (i.e., they cannot be broken down into more basic psychological components) [12][13][14]. On the other hand, the psychological constructionist approach [15][16][17] states that the same brain areas consistently activate across the instances of a range of emotion categories. In other words, this viewpoint maintains that no brain region is specifically dedicated to any particular emotion category [1]. Lindquist et al. [2] further categorize these approaches under three major hypotheses: the bipolarity hypothe...

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Advancing functional connectivity research from association to causation

Cited by 245 publications

References 93 publications

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Combining multiple functional connectivity methods to improve causal inferences

Emergence of the Affect from the Variation in the Whole-Brain Flow of Information

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