Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors

Abstract

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

“…Our results are aligned with those from studies that have taken a Bayesian approach to integrating structural and directed connectivity and shown that introducing a positive, monotonic mapping between structural connectivity and the variances of priors over effective or directed functional connectivity parameters, increase model evidence. 13,15,[17][18][19] More specifically, our work is aligned with prior work examining the impact of structural connectivity-based priors in group-level directed connectivity models. 15,18,19,22 It differs from this prior work, however, as these investigations have not, per se, investigated the impact of leveraging a structurally informed group-level effective connectivity to constrain subject-level effective connectivity.…”

“…First, a Bayesian approach that involves constraining the inversion of generative models with structural connectivity-based (i.e., structure-based) priors. [13][14][15][16][17][18][19][20][21][22] Second, a mechanistic approach, via which structural connectivity is incorporated directly into a generative model's equations (rather than being incorporated into priors over the equations' parameters). [23][24][25] Finally, a data-driven machine learning (ML) approach, which leverages various ML techniques to infer a map of directed interactions from both structural and functional connectivity taken together.…”

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

“…Indeed diffusion models have previously been proposed as a mechanism through which different neural populations communicate 35,48 , and it has been shown that diffusion processes can explain functional connectivity estimates 49 and model the propagation of activity evoked by intracranial stimulation more accurately than alternative communication models 36 . Furthermore, it has been shown that adding spatial information in the form of structural priors to standard VAR models and other FC measures can improve the estimation of FC networks [50][51][52][53] , which suggests that incorporating spatial information from the recording array into the GDAR model contributes to its improved performance.…”

Connectome Mapper 3: A Flexible and Open-Source Pipeline Software for Multiscale Multimodal Human Connectome Mapping

“…CMP3 has been successfully employed in a number of methodological (Akselrod et al, 2021;Glomb, Mullier, et al, 2020;Glomb, Rué Queralt, et al, 2020;Pascucci et al, 2021;Rué-Queralt et al, 2021;Zheng et al, 2020), clinical (Carboni et al, 2019(Carboni et al, , 2020(Carboni et al, , 2022, and data (Pascucci et al, 2022a(Pascucci et al, , 2022b research articles. CMP3 is part of the BIDS Apps, and also part of ReproNim/containers, a DataLad dataset with a collection of 40 popular containerized neuroimaging research pipelines, which allows one to easily include it as a subdataset within DataLad-controlled BIDS datasets, and achieve fully reproducible analysis by running CMP3 directly with DataLad.…”