Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

Bielczyk, Natalia; Uithol, Sebo; Mourik, Tim van; Anderson, Paul; Glennon, Jeffrey; Buitelaar, Jan K.

doi:10.1162/netn_a_00062

Cited by 64 publications

(64 citation statements)

References 191 publications

(412 reference statements)

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“…The causal inference framework based on DAGs discussed here provides an elegant and powerful theory of causality (Pearl, 2009;Peters et al, 2017) (although it should be noted that alternative operationalizations exist, see e.g. Bielczyk et al (2019), Cartwright (2007)). It is closely related to the notion of intervention (as described in Section 2.3.2), the idea that we can alter the behaviour of a system by setting certain variables to a particular value.…”

Section: Limitations -Is the World A Dag?mentioning

confidence: 99%

Node Centrality Measures are a poor substitute for Causal Inference

Dablander¹,

Hinne²

2018

Preprint

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Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Here, using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions.

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Section: Limitations -Is the World A Dag?mentioning

confidence: 99%

Node Centrality Measures are a poor substitute for Causal Inference

Dablander¹,

Hinne²

2018

Preprint

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“…There are various approaches to estimate connectivity direction, often divided into effective connectivity and directed functional connectivity (Friston, Moran, & Seth, 2013). Effective connectivity refers to the causal influence that one node exerts over another (Bielczyk et al, 2019;Friston, 2011), while directed functional connectivity (dFC) denotes information flow between nodes by estimating statistical interdependence using measured blood-oxygen-leveldependent (BOLD) responses (Bielczyk et al, 2019). Recent work has provided evidence of changes in connectivity direction with age.…”

Section: Introductionmentioning

confidence: 99%

Differences in directed functional brain connectivity related to age, sex and mental health

Lund

Alnæs

Schwab

et al. 2020

Human Brain Mapping

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Functional interconnections between brain regions define the “connectome” which is of central interest for understanding human brain function. Resting‐state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge‐wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

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“…There are various approaches to estimate connectivity direction, often divided into effective connectivity and directed functional connectivity (K. Friston, Moran, & Seth, 2013). Effective connectivity refers to the causal influence that one node exerts over another (Bielczyk et al, 2019; K. J. Friston, 2011), while directed functional connectivity (dFC) denotes information flow between nodes by estimating statistical interdependence using measured blood-oxygen-level-dependent (BOLD) responses (Bielczyk et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Effective connectivity refers to the causal influence that one node exerts over another (Bielczyk et al, 2019; K. J. Friston, 2011), while directed functional connectivity (dFC) denotes information flow between nodes by estimating statistical interdependence using measured blood-oxygen-level-dependent (BOLD) responses (Bielczyk et al, 2019). Recent work has provided evidence of changes in connectivity direction with age.…”

Section: Introductionmentioning

confidence: 99%

Differences in directed functional brain connectivity related to age, sex and mental health

Lund

Alnæs

Schwab

et al. 2019

Preprint

View full text Add to dashboard Cite

261 words 24 Main text body: 5240 words 25 Figures: 6 26 27 Keywords: 28 Directed functional brain connectivity 29 Cognitive aging 30 Dynamic graphical models 31 Resting-state fMRI 32 UK Biobank 33 HCP 34 35 36 2 Abstract 37 Objective: Functional interconnections between brain regions define the 'connectome' which 38 is of central interest for understanding human brain function, and is increasingly recognized in 39 the pathophysiology of mental disorders. Previous resting-state functional magnetic resonance 40 (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive 41 abilities and psychiatric symptoms, yet little is known how these factors may alter the 42 information flow. The commonly used approach infers functional brain connectivity using 43 stationary coefficients yielding static estimates of the undirected connection strength between 44 two brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic 45 coefficients reflecting directed temporal associations between network nodes, and can yield 46 novel insight into directed functional brain connectivity. Here, we aimed to validate the DGM 47 103 Dynamic graphical models (DGM) is a form of Dynamic Bayesian Networks, which 104 describes the instantaneous directed relationships between nodes (Bilmes, 2010; Schwab et 105 al., 2018). From this, one can study the spatiotemporal arrangement of links in the network, 106 defined here as the directionality between a node pair. This statistical method can give a 107 meaningful characterization of the dynamic connectivity between network nodes. Initial 108 implementation and validation of the approach in resting-state functional magnetic resonance 109

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Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

Cited by 64 publications

References 191 publications

Node Centrality Measures are a poor substitute for Causal Inference

Node Centrality Measures are a poor substitute for Causal Inference

Differences in directed functional brain connectivity related to age, sex and mental health

Differences in directed functional brain connectivity related to age, sex and mental health

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