Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions

© 2019 Massachusetts Institute of Technology..

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:3

Enthalten in:

Network neuroscience (Cambridge, Mass.) - 3(2019), 4 vom: 08., Seite 1009-1037

Sprache:

Englisch

Beteiligte Personen:

Bielczyk, Natalia Z [VerfasserIn]
Llera, Alberto [VerfasserIn]
Buitelaar, Jan K [VerfasserIn]
Glennon, Jeffrey C [VerfasserIn]
Beckmann, Christian F [VerfasserIn]

Links:

Volltext

Themen:

Causal inference
Effective connectivity
Functional magnetic resonance imaging
Journal Article
Pairwise causal inference

Anmerkungen:

Date Revised 14.10.2023

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.1162/netn_a_00099

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM302435549