A Cortical Folding Pattern-Guided Model of Intrinsic Functional Brain Networks in Emotion Processing

Abstract

There have been increasing studies demonstrating that emotion processing in humans is realized by the interaction within or among the large-scale intrinsic functional brain networks. Identifying those meaningful intrinsic functional networks based on task-based functional magnetic resonance imaging (task fMRI) with specific emotional stimuli and responses, and exploring the underlying functional working mechanisms of interregional neural communication within the intrinsic functional networks are thus of great importance to understand the neural basis of emotion processing. In this paper, we propose a novel cortical folding pattern-guided model of intrinsic networks in emotion processing: gyri serve as global functional connection centers that perform interregional neural communication among distinct regions via long distance dense axonal fibers, and sulci serve as local functional units that directly communicate with neighboring gyri via short distance fibers and indirectly communicate with other distinct regions via the neighboring gyri. We test the proposed model by adopting a computational framework of dictionary learning and sparse representation of emotion task fMRI data of 68 subjects in the publicly released Human Connectome Project. The proposed model provides novel insights of functional mechanisms in emotion processing.

Keywords: cortical gyri and sulci; emotion; functional model; intrinsic functional network; task fMRI.

Figure 1

An example intrinsic network composed of two distinct regions of interest (ROI 1 and 2 in left and right hemisphere, respectively) viewed in volume space (A) and on cortical surface (C). (B) The unknown functional working mechanisms of interregional neural communication within one network. (D) Illustration of the proposed model. The red/green color represents the gyral/sulcal regions within the ROI of intrinsic network.

Figure 2

The illustration of sparse representation of whole-brain rsfMRI signals. (A) The whole-brain rsfMRI signals of an example subject which are aggregated into a 2D matrix X. (B) The decomposed dictionary matrix D and sparse coefficient matrix α based on X. (C) The identified intrinsic networks in task fMRI volume space. (D) The corresponding intrinsic networks on cortical surface.

Figure 3

Emotion task fMRI signal representation accuracy difference between gyral and sulcal regions in default mode network (DMN) of one subject. The detailed assessment of each of the four distinct regions (ROI 1–4) within DMN is in zoomed-in view. G, gyri; S, sulci. P-value: two-sample one-tailed t-test (gyri > sulci, p = 0.05, Bonferroni corrected).

Figure 4

Emotion task fMRI signal representation accuracy difference between gyral and sulcal regions in the other eight intrinsic networks.