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Methods for decoding cortical gradients of functional connectivity

Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline J-B, Dockès J, Kent JD, Bartley JE, Flannery JS, Hill-Bowen LD, Lobo RP, Poudel R, Ray KL, Robinson JL, Laird RW, Sutherland MT, de la Vega A, Laird AR, GSAW (2024).

Abstract

Background

  • Macroscale gradients of brain connectivity have emerged as a central principle for understanding functional brain organization.
  • The functional significance and interpretation of gradients remain a central topic of discussion in the neuroimaging community.
  • Previous studies have demonstrated that the gradients may be described using meta-analytic functional decoding techniques.
  • However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance.

Goals

Overall Objective: investigate and improve the framework of data-driven methods for decoding the principal gradient of functional connectivity.

  • Examine and evaluate different methods for decoding brain maps on surface space.
  • Establish a principled approach for gradient segmentation and meta-analytic decoding.
  • Provide recommendations on best practices and develop flexible methods for gradient-based functional decoding.

Methods

We used the resting-state fMRI (rs-fMRI) group-average dense connectome from the Human Connectome Project (HCP) S1200 data release to identify the principal gradient of functional connectivity. We evaluated three segmentation approaches: (i) percentile-based, (ii) segmentation based on a 1D k-means clustering approach, and (iii) segmentation based on the Kernel Density Estimation curve of the gradient axis. We assessed six different decoding strategies that used two meta-analytic databases (i.e., Neurosynth and NeuroQuery) and three methods to produce meta-analytic maps (i.e., term-based, LDA-based, and GC-LDA-based decoding). In addition, we proposed a method for decoding lower-order gradient maps combined with the principal gradient in a high-dimensional space.

Results

  • For small numbers of segments, a k-means algorithm yields the most confident distribution of boundaries, as shown by the silhouette coefficients, variance ratio, and cluster separation.
  • LDA-based produced meta-analytic maps that yielded a relatively high correlation value and a collection of terms that naturally improved the information content, TFIDF, and SNR.
  • NS and NQ performed similarly regarding their correlation profile.
  • We reproduced the results from Margulies et al., showing the continuous transition from primary sensorimotor to transmodal regions.
  • We proposed methods for decoding lower-order gradient maps.

Conclusions

  • We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding the principal gradient of functional connectivity.
  • This combination of approaches and our recommended visualization method for reporting meta-analytic decoding findings will enhance the overall interpretability of macroscale gradients in the fMRI community.