Abstract
Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.
Highlights
We identified the right fusiform face area (rFFA) by the right fusiform face complex region in the human connectome project (HCP)-MMP1.0 (Glasser et al, 2016)
In order to better characterize the brain region’s function of individuals, our study proposed that a brain region’s function is represented by the multi-hops connectivity profiles
We tested our proposal by predicting the functional face activation of the right fusiform face complex (rFFC) region via the rFFC region’s multihops functional connectivity
Summary
Brain connectivity acts as the pathway for transferring information between brain regions and determines the information inflow and outflow of each cortical region. Passingham et al (2002) proposed that the function of each cortical region can be determined by the region’s input and output connectivity profiles. Mars et al (2018) further tested and extended this proposal via the neuroimaging of connectivity, and showed that the connectivity space composed by each region’s connectivity profiles provides a powerful framework in describing a brain region’s function.The connectivity profile can be defined in terms of the white matter pathway represented by tractography through diffusion magnetic resonance imaging (MRI), or in terms of the temporal coupling between spontaneous fluctuations of resting-state functional MRI (rfMRI) signal. Passingham et al (2002) proposed that the function of each cortical region can be determined by the region’s input and output connectivity profiles. Though the proposal that a brain region’s function is represented by the input and output connectivity profiles is widely adopted in various studies, this proposal is deficient in characterizing the individual differences of a target brain region’s function. Under this proposal, a brain region’s function can be represented by a linear combination of the region’s connectivity profiles This representation only utilizes direct connectivity profiles and neglects the individual differences in the functional information of neighboring regions. Since these individual differences in neighboring regions can transfer to the target brain region via the direct connections, neglecting these individual differences is not beneficial for characterizing target brain region’s function
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