Effective Connectivity Study Guiding the Neuromodulation Intervention in Figurative Language Comprehension Using Optical Neuroimaging.

Tania Alexandra Couto,Yutao Xiang,Meng-Yun Wang,Zhen Yuan,Juan Zhang,Karl Neergaard,Paulo Armada Da Silva,Chenggang Wu,Shiyang Xu

doi:10.1155/2020/8882207

Abstract

The current study is aimed at establishing links between brain network examination and neural plasticity studies measured by optical neuroimaging. Sixteen healthy subjects were recruited from the University of Macau to test the Granger Prediction Estimation (GPE) method to investigate brain network connectivity during figurative language comprehension. The method is aimed at mapping significant causal relationships across language brain networks, captured by functional near-infrared spectroscopy measurements (fNIRS): (i) definition of regions of interest (ROIs) based on significant channels extracted from spatial activation maps; (ii) inspection of significant causal relationships in temporal resolution, exploring the experimental task agreement; and (iii) early identification of stronger causal relationships that guide neuromodulation intervention, targeting impaired connectivity pathways. Our results propose top-down mechanisms responsible for perceptive-attention engagement in the left anterior frontal cortex and bottom-up mechanism in the right hemispheres during the semantic integration of figurative language. Moreover, the interhemispheric directional flow suggests a right hemisphere engagement in decoding unfamiliar literal sentences and fine-grained integration guided by the left hemisphere to reduce ambiguity in meaningless words. Finally, bottom-up mechanisms seem activated by logographic-semantic processing in literal meanings and memory storage centres in meaningless comprehension. To sum up, our main findings reveal that the Granger Prediction Estimation (GPE) integrated strategy proposes an effective link between assessment and intervention, capable of enhancing the efficiency of the treatment in language disorders and reducing the neuromodulation side effects.

Highlights

The neuroimaging paradigm has been changing, and a new analysis has emerged to facilitate the understanding of the brain mechanisms
The first functional neuroimaging trend relies on brain function identification based on brain activation between different brain regions [1], while the second trend has emerged from functional connectivity measures between different brain regions in a single brain
The current study proposes a Granger Prediction Estimation (GPE) method to address the potential of causal analysis

Summary

Introduction

The neuroimaging paradigm has been changing, and a new analysis has emerged to facilitate the understanding of the brain mechanisms. The causal analysis adds more accuracy to the connectivity study, which allows us to predict how one brain region of interest may influence another one In this sense, the current study proposes a Granger Prediction Estimation (GPE) method to address the potential of causal analysis. The functional and effective connectivity studies guided by functional near-infrared spectroscopy (NIRS) have been recently described as a promising technique [2, 4] to map language networks and provide a more reliable inspection to guide the neural plasticity studies. The Granger Prediction Estimation (GPE) method is proposed for fNIRS data to identify brain networks, explaining how one specific neural mechanism might exert influence on another [18, 19] and illustrate how the causal mappings might be crucial to address the neuromodulation protocols in a healthy or clinical population

Materials and Methods

Results

Discussion and Conclusions